DEV Community: Tim

RenderProof: A video delivery agent that refuses to guess

Tim — Sun, 20 Sep 2026 16:33:57 +0000

This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content.

What I Built

RenderProof is a video delivery planning agent. It helps creators and video engineers choose codec, container, and FFmpeg settings without pretending that every recommendation is equally trustworthy.

The problem is not a lack of video encoding advice. The problem is that advice has scope, versions, dependencies, and conflicts. A rule can be valid for one codec but not another, for one container but not another, or for one source but not the next. A generic chatbot can produce a plausible FFmpeg command while silently mixing incompatible constraints.

RenderProof takes a different approach. It treats Sanity as the source of truth for structured claims, compatibility rules, delivery profiles, pipeline recipes, and editorial decisions. Before it answers, the agent retrieves those records through Sanity Context MCP. If the Knowledge Base does not establish a fact, RenderProof says so.

One deliberately tested example is libx264 with nal-hrd=cbr and MP4. The captured FFmpeg documentation says the combination is not allowed in MP4. Another Knowledge Base source suggested MKV or MPEG-TS as a fallback. RenderProof surfaced the conflict instead of choosing whichever answer sounded more convenient. I resolved the conflict in Sanity by selecting the claim supported by the current evidence: no fallback container was established by the indexed source. That decision is now preserved as a Knowledge Base instruction for future rebuilds.

Demo

Live application:

https://renderproof.proteinpayment.com

Try:

Can I use libx264 nal-hrd=cbr for MP4, and is any fallback container established by the Knowledge Base?

The interface shows the actual MCP tool activity while the answer streams. In the tested path it reaches initial_context and knowledge_base_read.

Code

Repository:

https://github.com/TZ005/renderproof

The repository contains:

web/: Next.js application, DeepSeek integration, MCP client, streaming API, and UI
studio/: Sanity Studio schemas and seeded content
sources/: captured FFmpeg source documents used by the Knowledge Base
docs/: challenge checklist, demo script, and submission draft

How I Used Sanity

Structured content

The Sanity dataset models:

sourceDocument
sourceClaim
codecProfile
containerProfile
compatibilityRule
deliveryProfile
pipelineRecipe
decision

This structure matters because the answer depends on relationships. A compatibility rule points to the codec and container it applies to. A claim points to its original source and a stable locator. A delivery profile references the codec, container, and constraints that shape the recommendation. A decision selects a claim when sources conflict.

The seed dataset contains 29 documents. The Knowledge Base contains 14 built entries across three sources: the structured Sanity dataset, the FFmpeg Codecs documentation, and the FFmpeg Formats documentation.

Sanity Context MCP

The server connects to a hosted Sanity Context MCP endpoint with an organization-level Context Viewer token. The token remains server-side and is stored as a Cloudflare Worker secret in production.

The agent uses the Vercel AI SDK MCP client, exposes MCP tools to DeepSeek, and runs a multi-step tool loop. The browser receives NDJSON events so it can display:

which MCP tools are running
which tools completed
the streamed answer
the citations included by the model

Knowledge Base decisions

The conflict between "use MPEG-TS" and "no fallback is established" is the most useful demonstration of the approach. Sanity Context placed the claims side by side. I selected the evidence-bounded claim, resolved the issue, and the result became a standing instruction.

That means the important behavior is not only a prompt instruction. It is represented as data and carried through future builds.

Sanity Project Details

Project ID:

84r0129y

Dataset:

production

Agent Session

RenderProof: Sanity Context MCP build

Agent

我先看一下当前工作区，再结合截图里的 Sanity 挑战给你一个能真正落地的方案。

Agent

▸ exec_command {"cmd":"Get-ChildItem -Force | Select-Object Mode,Length,LastWriteTime,Name; ...

Input

$ {"cmd":"Get-ChildItem -Force | Select-Object Mode,Length,LastWriteTime,Name; rg --files -g '!node_modules' -g '!dist' -g '!build' | Select-Object -First 200","justification":"","login":true,"max_outpu...

Agent

这个截图是 DEV × Sanity Challenge。核心是 16 天内提交一个真正使用 Sanity 结构化内容、Content MCP 或 Knowledge Base 的作品：

Path One（推荐）：做一个能查询真实 Knowledge Base 的 Agent，评审更看重内容结构、技术实现和可用性。
Path Two：做有创意、甚至有点怪的 vibe-coded 应用，更看重创意和构建过程。

截止时间是 10 月 4 日 23:59 PDT，也就是 北京时间 10 月 5 日 14:59。

我能直接承担完整参赛流程：设计 Sanity schema 和 dataset、导入种子内容、搭 Knowledge Base、接 Content MCP、实现 Agent 和前端、部署、测试、录 Agent session、准备截图，最后按模板写好 DEV 参赛文章和 #sanitychallenge 标签。

结合当前的 G:\视频处理工具 目录，我建议做 Path One 的 Video Pipeline Copilot：它从 Sanity Knowledge Base 查询编码格式、平台限制、分辨率和兼容性规则，然后给用户生成带风险说明的 FFmpeg 方案与命令。这个方向比通用聊天机器人更具体，评审也容易看到 structured content 的价值。

你准备走 Path One 还是 Path Two？没有指定的话，我会按 Path One 的 Video Pipeline Copilot 开始搭建。

我再用原图核对一遍，尤其是小字和链接部分；截图能看懂，不等于可以直接替代官方文档。

Agent

▸ view_image {"detail":"original","path":"C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\codex...

Input

{
  "detail": "original",
  "path": "C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\codex-clipboard-74088206-5499-4760-a1ca-5832f4f0b846.png"
}

Agent

能看懂，但不能说“不用看里面内容了”。

我已经看懂截图里的主要内容：两条赛道、评审标准、$500 × 5 的奖项、提交到 DEV、必须带 #sanitychallenge、10 月 4 日 23:59 PT 截止，以及需要嵌入 Agent Session、阅读 rules/FAQ 等要求。

但看懂截图和能准确完成参赛作品是两回事。截图没有包含实际链接、提交模板、FAQ、官方规则和最新 Sanity API 文档。真正开始前还必须核对：

Sanity Context、Day One、Knowledge Bases、App SDK 的当前用法
Content MCP 的接入方式和鉴权要求
官方 submission template、评审细则和资格要求
Agent Sessions 的嵌入规则
提交时对仓库、演示地址和凭据的具体要求

所以现在只能说：挑战意图我理解没问题，但还不能跳过官方原文直接开发。 把这篇 DEV 文章的链接发我，或者允许我按标题去搜；我先做一份准确的规则与资料核对清单，再开始搭 Path One。

我现在读取原文和其中的官方链接，重点核对规则、模板、评审标准与截止时间。

Agent

▸ exec_command {"cmd":"$response = Invoke-WebRequest -Uri 'https://dev.to/devteam/join-the-s...

Input

$ {"cmd":"$response = Invoke-WebRequest -Uri 'https://dev.to/devteam/join-the-sanity-challenge-2500-in-prizes-for-five-winners-514m' -UseBasicParsing; \"STATUS=$($response.StatusCode)\"; \"TITLE=$(([reg...

Agent

原文已能访问。我继续把正文、官方挑战页和提交模板拉全，避免只依赖截图或链接摘要。

Agent

▸ exec_command {"cmd":"$uri = 'https://dev.to/api/articles/by_path?url=/devteam/join-the-san...

Input

$ {"cmd":"$uri = 'https://dev.to/api/articles/by_path?url=/devteam/join-the-sanity-challenge-2500-in-prizes-for-five-winners-514m'; $a = Invoke-RestMethod -Uri $uri; \"ID=$($a.id)\"; \"TITLE=$($a.title)...

Agent

▸ exec_command {"cmd":"$response = Invoke-WebRequest -Uri 'https://dev.to/challenges/sanity-...

Input

$ {"cmd":"$response = Invoke-WebRequest -Uri 'https://dev.to/challenges/sanity-2026-09-16' -UseBasicParsing; \"STATUS=$($response.StatusCode)\"; \"TITLE=$(([regex]::Match($response.Content, '<title>(.*?...

Agent

规则页已经读完。页面里还有一个容易漏掉的硬性条件：提交必须公开 Sanity Project ID 或数据集链接，否则可能直接算不完整；我再核对提交模板和正式规则。

Agent

▸ exec_command {"cmd":"$response = Invoke-WebRequest -Uri 'https://dev.to/challenges/sanity-...

Input

$ {"cmd":"$response = Invoke-WebRequest -Uri 'https://dev.to/challenges/sanity-2026-09-16' -UseBasicParsing; $html = $response.Content; $matches = [regex]::Matches($html, 'href=\"([^\"]+)\"[^>]*>(.*?)</...

Agent

我再确认参赛资格和限制条款，特别是 18+、地区限制、团队人数与英文提交要求。

Agent

▸ exec_command {"cmd":"$r = Invoke-WebRequest -Uri 'https://dev.to/page/sanity-challenge-v26...

Input

$ {"cmd":"$r = Invoke-WebRequest -Uri 'https://dev.to/page/sanity-challenge-v26-09-16-contest-rules' -UseBasicParsing; \"STATUS=$($r.StatusCode)\"; $t = $r.Content -replace '<script[\\s\\S]*?</script>',...

Agent

▸ exec_command {"cmd":"$r = Invoke-WebRequest -Uri 'https://dev.to/page/official-hackathon-r...

Input

$ {"cmd":"$r = Invoke-WebRequest -Uri 'https://dev.to/page/official-hackathon-rules' -UseBasicParsing; $t = $r.Content -replace '<script[\\s\\S]*?</script>',' ' -replace '<style[\\s\\S]*?</style>',' ' -...

Agent

▸ exec_command {"cmd":"$r = Invoke-WebRequest -Uri 'https://dev.to/page/official-hackathon-r...

Input

$ {"cmd":"$r = Invoke-WebRequest -Uri 'https://dev.to/page/official-hackathon-rules' -UseBasicParsing; $t = $r.Content -replace '<script[\\s\\S]*?</script>',' ' -replace '<style[\\s\\S]*?</style>',' ' -...

Agent

已经读完。我核对了原文、官方挑战页、提交模板、专属规则和通用规则。确实有几个截图里没有、但会影响参赛资格的关键点：

作品必须在 2026 年 9 月 18 日 9:00 PDT 之后开始开发，不能把之前已经做好的项目直接拿来提交。
参赛者需 18 岁以上、拥有状态正常的 DEV 账号。中国不在明确排除地区名单里，但受当地法律和美国出口限制约束。
Path One 必须使用 Sanity Context MCP，并连接到 Knowledge Base。普通向量搜索或纯聊天机器人不算切题。
评审明确强调：如果关键词搜索也能得到同样答案，就不够好。最强作品必须依赖结构化内容、来源追踪、知识之间的关系，甚至能并列展示互相矛盾的来源。
提交必须提供 Sanity Project ID 或公开 Dataset URL，否则可能被判定为不完整。
需要给评委提供登录凭据或测试说明；帖子必须带 #sanitychallenge。
Path One 和 Path Two 要分别发帖，每个赛道最多一篇。团队最多 4 人。
英文提交才能参与评奖；非英文只能拿完成徽章。
必须使用模板中的 What I Built / Demo / Code / How I Used Sanity / Sanity Project Details / Agent Session 结构。
截止时间是 10 月 4 日 23:59 PDT，即 北京时间 10 月 5 日 14:59。
$500 奖金共有 5 份：Path One 三名，Path Two 两名。平局时会比较 DEV 帖子的正向互动数。

我的建议仍然是 Path One，并且新建一个独立项目，不复用比赛开始前的工作。下一步应先读 Sanity Context、Knowledge Base 和 MCP 的当前文档，确认账号、数据结构和部署方式，然后从头搭建一个以结构化内容为核心、离开 Knowledge Base 就无法工作的 Agent。

看清楚了，这是 DEV 的 Path One 提交模板，和官方挑战页里的模板一致。

模板包含这些固定部分：

标题与标签：devchallenge、sanitychallenge、sanity、ai
参赛声明：注明提交到 Path One
What I Built：项目解决什么问题、体验是什么
Demo：部署链接、视频演示或截图
Code：代码仓库链接
How I Used Sanity：Agent 如何读取和查询 Sanity，Knowledge Base 指向什么内容，用了哪些 Sanity Context 工具
Sanity Project Details：必填，提供 Sanity Project ID 或公开 Dataset URL
Agent Session：可选但强烈建议，上传 Codex/Claude Code 等构建记录，并设为公开
Cover Image：可选封面
Team Submissions：多人参赛时列出所有 DEV 用户名

关键要求是：模板不是只把占位符删掉就完事，How I Used Sanity 必须真正解释结构化内容为何必要，以及 Agent 如何通过 MCP 和 Knowledge Base 使用它。

后面我会按这个模板写最终英文提交帖，并把 Demo、仓库、Sanity Project ID 和 Agent Session 分别补齐。

懂。我已经不是只看截图，而是核对了挑战页、Path One 模板、专属规则和通用规则。后面会把下面这些视为硬约束：

参赛资格
- 18 岁以上，拥有状态正常的 DEV 账号。
- 中国不在明确排除地区内，但仍受当地法律和美国出口限制约束。
- 团队最多 4 人；只能由一人发布团队作品，并列出全部成员的 DEV 用户名。
- 项目必须在挑战开始后开发。Path One 必须从新项目开始，不能直接提交挑战前已经完成的作品。开始时间是 2026 年 9 月 18 日 9:00 PDT，即北京时间 9 月 19 日 00:00。

Path One 要求
- 必须构建 Agent，并连接 Sanity Context MCP。
- MCP 后端必须是 Sanity Knowledge Base。
- 必须实际体现结构化内容、内容关系和来源溯源的不可替代性。
- 普通的关键词搜索或通用聊天机器人不算达标。
- 评审四项：Sanity Context 和结构化内容的使用价值、技术实现和代码质量、Knowledge Base 的使用、可用性。

提交要求
- 在 DEV 发布英文文章，否则只能获得完成徽章，不能评奖。
- 必须使用 Path One submission template。
- 必须包含 #sanitychallenge 标签。
- 必须公开 Sanity Project ID 或公开 Dataset URL，否则可能被判定为不完整。
- 如果应用需要登录，必须提供测试账号或清晰的测试说明。
- Agent Session 可选但强烈建议；上传后必须设为公开，并先清除密钥和敏感信息。
- Path One 和 Path Two 必须分别发帖。
- FAQ 明确说每个赛道只能提交一次。专属规则写了“不限制 Entry 数量”，存在表述冲突；执行时按更严格的 每个赛道只提交一篇，最安全。

时间与奖项
- 提交截止：2026 年 10 月 4 日 23:59 PDT，即北京时间 10 月 5 日 14:59。
- 获奖公布：2026 年 10 月 22 日。
- Path One 产生 3 名获奖者，每人 $500、DEV++ 和专属徽章。
- 平局时比较 DEV 帖子的正向互动数。
- 获奖后可能需要在规定时间内签署资格声明并提供税务信息。

唯一需要谨慎处理的规则冲突就是“提交次数”。其他要求已经明确。我会按最严格标准执行，并在正式提交前再做一次规则核对。

方案要先按风险顺序来。我先快速核对 Sanity Context、Knowledge Base 和 MCP 的实际接入方式，避免给你一个听起来合理但技术上跑不通的计划。

Agent

▸ exec_command {"cmd":"$u='https://www.sanity.io/docs/ai/sanity-context'; $r=Invoke-WebReque...

Input

$ {"cmd":"$u='https://www.sanity.io/docs/ai/sanity-context'; $r=Invoke-WebRequest -Uri $u -UseBasicParsing; \"STATUS=$($r.StatusCode) URL=$u\"; $t=$r.Content -replace '<script[\\s\\S]*?</script>',' ' -r...

Agent

▸ exec_command {"cmd":"$u='https://www.sanity.io/docs/ai/sanity-context-knowledge-bases'; $r...

Input

$ {"cmd":"$u='https://www.sanity.io/docs/ai/sanity-context-knowledge-bases'; $r=Invoke-WebRequest -Uri $u -UseBasicParsing; \"STATUS=$($r.StatusCode) URL=$u\"; $t=$r.Content -replace '<script[\\s\\S]*?<...

Agent

▸ exec_command {"cmd":"$u='https://www.sanity.io/docs/next-js-quickstart'; $r=Invoke-WebRequ...

Input

$ {"cmd":"$u='https://www.sanity.io/docs/next-js-quickstart'; $r=Invoke-WebRequest -Uri $u -UseBasicParsing; \"STATUS=$($r.StatusCode) URL=$u\"; $t=$r.Content -replace '<script[\\s\\S]*?</script>',' ' -...

Agent

如果要做，我不会先写页面，而是按“技术风险优先”的顺序执行。目标是 Path One，项目我会选一个结构化内容真正不可替代的题目。

项目方向：RenderProof

一个“视频交付管线 Agent”。用户给出源视频参数和目标平台，Agent 从 Sanity Knowledge Base 查询平台限制、编码器兼容性、HDR/帧率/音频规则和历史变更，然后输出可执行的 FFmpeg 管线。

它不是普通问答，因为必须同时满足：

多组结构化约束和兼容关系
不同版本、不同来源的规定
来源冲突和适用范围
用户的优先级决策，例如兼容性优先还是体积优先
每个结论都必须有来源，不允许模型编造参数

技术结构
- Sanity Studio 定义 schema，使用 Studio 5.1+ 并部署 schema。
- 数据模型包含 SourceDocument、SourceClaim、PlatformProfile、Codec、Container、DeliveryConstraint、CompatibilityRule、PipelineRecipe 和 Decision。
- Knowledge Base 同时索引 Sanity Dataset、官方平台文档和编码器文档。
- 建立两个 Context MCP 能力：Knowledge Base 模式用来查带引用的文档，GROQ 模式用来查询类型化字段和兼容关系。
- Agent 使用 Next.js + TypeScript + Vercel AI SDK，集成 Sanity Context MCP。
- FFmpeg 命令由结构化 Recipe 和确定性校验器生成，不让模型直接猜命令。
- UI 分三步：输入媒体参数、查看证据和冲突、生成并解释管线。
- 当两个来源冲突时，界面并列展示 claim、版本、日期和来源；用户选定 ground truth 后写入 Knowledge Base Instructions。
- 如果缺少必要约束，Agent 应停止并说明缺什么，而不是给出看似合理的错误参数。

执行顺序
1. 今天先打通权限：启用 Sanity Context 和 Knowledge Bases Beta，创建带 Context Viewer 权限的组织 Token。这是最大的外部阻塞，必须先验证。
2. 最小 MCP 验证：新建独立仓库，搭一个最小 Agent，只完成一次 MCP 连接、读取 initial context 和读取一条 Knowledge Base entry。
3. 建立内容模型：设计 schema、导入种子数据，并为 Knowledge Base 写明确的 purpose 和冲突处理 Instructions。
4. 实现领域逻辑：完成约束过滤、兼容性检查、来源追踪和 FFmpeg recipe 校验。
5. 接入 Agent：让 Agent 先读 Knowledge Base outline，再选择 entry，必要时调用 GROQ 查询，最后把证据交给确定性引擎。
6. 完成前端：重点展示证据、冲突、决策和最终命令，不把时间浪费在装饰性页面上。
7. 测试：准备至少 20 个黄金案例，包括缺数据、冲突数据、HDR 转 SDR、不同平台限制和错误输入，检查是否会产生无依据答案。
8. 部署与演示：部署公开 Demo、准备测试凭据、录制短视频、截取关键 Agent Session 并设为公开。
9. 写提交文章：按模板完成英文帖子，填入 Demo、Code、How I Used Sanity、Sanity Project ID 和 Agent Session。
10. 10 月 2 日前完成提交，留出两天处理账号、权限或公开链接问题。

整个过程中需要一个全新的独立仓库，不能使用比赛开始前已有的代码。主要会用到你的 Sanity 管理员账号、模型 API Key、DEV 账号和 GitHub/部署账号；密钥只放环境变量，不能写进仓库或 Agent Session。

可以，我先把 Sanity 用最简单的话讲清楚。

Sanity 是什么
你可以把它理解成：

一个专门存放内容数据的云端后台，同时提供编辑界面、数据库和给程序或 AI 读取数据的接口。

比如一个视频交付项目里，我们可以把“YouTube 最大时长”“H.264 支持哪些参数”“某条规则来自哪个官方页面”“两个来源是否冲突”都存成有字段、有关联的数据，而不是一堆 Word 文档。

几个名词对应关系：

Sanity Studio：你以后登录后看到的编辑后台。
Content Lake：Sanity 的云数据库。
GROQ：查这个数据库的语言。
Sanity Context：让 AI Agent 读取 Sanity 内容的官方接口。
Knowledge Base：从文档、网站和 Sanity 数据中整理出的 Agent 参考知识库。
MCP：可以理解为 AI 和外部服务之间的标准插头。Agent 通过 MCP 读取 Sanity。

挑战要求我们真正使用 Sanity，不能用 Notion、普通文件或自己的数据库替代这条路。

谁做什么
你负责账号、授权和最终发布，我负责技术实现。

你需要做的主要是：

注册一个 Sanity 账号，并按照我的指引创建组织、项目和 API Token。
确认自己满 18 岁，并注册 DEV 账号；最终参赛文章必须从你的 DEV 账号发布。
准备一个模型 API Key，例如 OpenAI、Claude 或 Gemini。密钥只放进本地 .env，不要发给我，也不要在聊天里发送。
以后创建 GitHub 和部署平台账号，用于保存代码和上线 Demo。
在提交前，把公开链接和测试凭据提供给我检查。
最后由你点击发布，因为文章必须属于你的 DEV 账号。

我负责：

创建全新代码项目
设计 Sanity schema 和知识库结构
导入内容、处理来源和冲突
编写 Agent、前端、测试和部署配置
准备演示视频、截图和 Agent Session
起草完整英文提交文章
做最后规则检查和提交前审计

你现在先告诉我三件事：是否满 18 岁、有没有 Sanity 和 DEV 账号、有没有可用的模型 API Key。 后面的每一步我会带着你操作，不需要你先学会 Sanity。

DeepSeek 可以作为模型，但先要确认它是否支持工具调用；Sanity 则是挑战强制要求的另一层，不能由 DeepSeek 替代。我先核对当前 API 兼容性。

Agent

▸ exec_command {"cmd":"$urls=@('https://api-docs.deepseek.com/','https://api-docs.deepseek.c...

Input

$ {"cmd":"$urls=@('https://api-docs.deepseek.com/','https://api-docs.deepseek.com/guides/function_calling','https://api-docs.deepseek.com/api/create-chat-completion'); foreach($u in $urls){ try { $r=Inv...

Agent

▸ exec_command {"cmd":"npm view @ai-sdk/deepseek version description peerDependencies --json...

Input

$ {"cmd":"npm view @ai-sdk/deepseek version description peerDependencies --json","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"ba...

Agent

▸ exec_command {"cmd":"npm view @ai-sdk/openai-compatible version description --json","justi...

Input

$ {"cmd":"npm view @ai-sdk/openai-compatible version description --json","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty...

Agent

▸ exec_command {"cmd":"$urls = @('https://api-docs.deepseek.com/','https://api-docs.deepseek...

Input

$ {"cmd":"$urls = @('https://api-docs.deepseek.com/','https://api-docs.deepseek.com/guides/function_calling','https://api-docs.deepseek.com/api/create-chat-completion'); foreach ($u in $urls) { try { $r...

Agent

可以用 DeepSeek 作为 AI 模型，但不能用它替代 Sanity。这两个负责的事情不同：

Sanity：存放挑战要求的知识和结构化内容，是数据来源。
DeepSeek：负责理解问题、选择工具、组织答案。
MCP：连接 DeepSeek Agent 和 Sanity，让 Agent 能读取 Sanity 内容。
RenderProof：我们写的程序，把这三部分组合成完整产品。

可以把它理解成：Sanity 是资料库，DeepSeek 是负责查资料和回答的人，MCP 是借书窗口，RenderProof 是完整服务。

DeepSeek 官方 API 支持 OpenAI 兼容格式、工具调用、JSON 输出，因此可以接入 Vercel AI SDK，再由 Agent 调用 Sanity Context MCP。模型可以使用 deepseek-flash 或 deepseek-v4-pro。它会有按量计费的 API 成本，不是免费无限使用。

你现在需要做三件事：

打开 Sanity 注册账号。先按普通免费账号开始，Context 和 Knowledge Base 的权限以后再一起确认。
打开 DeepSeek Platform 注册并创建一个 API Key。不要把 Key 发到聊天里，之后放在本地 .env 文件。
打开 DEV 注册账号，因为最终参赛文章必须从你的 DEV 账号发布。

先完成 Sanity 注册即可。完成后告诉我，我会从创建项目、启用 Context、建立 Knowledge Base 开始一步步带你操作。

选 Personal。

这个比赛是你的个人项目，不是公司客户项目，也不是代运营机构的项目。选 Personal 后继续下一步即可。

选 Other，然后填写：

DEV Sanity Challenge

选 I'm not a developer。

这个选项只影响 Sanity 给你的入门提示，不会影响参赛。JavaScript、Agent 和页面代码都由我来完成。

选 Product / App。

我们要做的是完整的 Agent 应用；Knowledge Base 是它背后的数据能力，后面会单独创建和配置。

填写：

RenderProof

然后点击 Create project。

同时选择：

React
TypeScript
Next.js

然后点击 Next。

选 No, building from scratch。

我们必须建立全新的比赛项目，不能把现有项目接进来。

选 No, starting from scratch。

看到了，项目已经创建成功：

项目名：RenderProof
Project ID：8z4129zy
Organization ID：oey86fpbi
当前方案：Growth Trial，还有 30 天
前端技术：Next.js

Project ID 和 Organization ID 可以公开，不是密钥。现在先点击页面左下角的 Complete setup。

然后继续做两个关键设置：

进入组织管理里的 Labs，启用：
- Sanity Context
- Knowledge Bases
进入 Organization > API > Tokens，创建一个组织级 Token：
- 名称：RenderProof Context Viewer
- 权限：Context Viewer
- 不要把 Token 发到聊天里，保留在自己的电脑上。

完成这两步后告诉我。如果找不到 Labs 或 Organization API Tokens，把下一张截图发我。

Viewer 选得对，它是最小只读权限，不要选 Developer。

但要确认现在是在 Organization 组织级 API 页面，不是项目自己的 API 页面。Sanity Context 和 Knowledge Base 是组织级功能，需要组织级 Token。如果这个弹窗是从项目的 API 标签打开的，就先取消。

正确路径应是：

Tofu / 组织管理 > Manage > API > Tokens

启用 Context 后，权限里应出现 Context Viewer。如果没有这个角色，Viewer 也可以用。到期时间建议设置到 2026 年 10 月 6 日，给提交留一点缓冲。

现在可以点击 Add token，但生成后不要把 Token 发给我，也不要把截图里的完整 Token 发出来。

不是这个。你现在打开的是 项目级 API Token，只用于访问 RenderProof 这个项目的数据；挑战需要的 Context MCP 和 Knowledge Base 使用的是 组织级 API Token。

先不要在这里创建，返回上一层，然后进入：

Tofu（左上角组织名） > Manage > API > Tokens

如果直接打开组织管理页，可以尝试：

https://www.sanity.io/manage/organization/oey86fpw1

先在组织管理的 Labs 中启用：

Sanity Context
Knowledge Bases

然后进入组织级的 API > Tokens，创建 Token。权限选择 Context Viewer；没有这个选项时选择 Viewer，有效期建议设置到 2026 年 10 月 6 日。

如果组织管理页里找不到 Labs 或 API，把那个页面截图发我。项目级 API 页面先不用管。

对，这里就是组织管理页面。

先点击 Labs，不要直接点 API。在 Labs 里查找并启用：

Sanity Context
Knowledge Bases

启用后再点击 API，进入组织级 Token 页面创建 Token。

先进入 Labs，把看到的选项截图发我，我再告诉你具体打开哪些。

这里的 Context 已经启用了，绿色勾表示可用。Knowledge Bases 很可能包含在 Context 的 Dashboard 里。

现在点击 Open in dashboard，然后把你看到的第一个页面截图发我。接下来我们要在 Context 里创建 Knowledge Base 和 MCP 端点。

点击上面的 Labs，进入后找到 Context，再点击它右侧的 Open in dashboard。

不要点 Overview 里的蓝色 Open Dashboard，那个不是 Context 的入口。

对，这就是 Context 的 Dashboard。

点击左上角或中间的 New knowledge base。下一页会让我们填写名称、用途和知识来源，先打开表单，把截图发我后再继续。

把表单填成下面这样：

Title

RenderProof Video Delivery

Purpose

Help creators and video engineers choose trustworthy transcoding and delivery settings. Lead with platform limits, codec and container compatibility, HDR-to-SDR rules, hardware constraints, source provenance, and conflicting recommendations. Leave out marketing copy, pricing, non-video platform features, and general editing tutorials.

然后点击 Create knowledge base。下一页添加来源时截图给我。

先别急着把整个网站都爬进来。我核对一下 Knowledge Base 的 Website、Files 和 Dataset 三种来源的抓取范围，再决定现在选哪种。

Agent

▸ exec_command {"cmd":"$urls = @('https://www.sanity.io/docs/ai/sanity-context-knowledge-bas...

Input

$ {"cmd":"$urls = @('https://www.sanity.io/docs/ai/sanity-context-knowledge-base-source-types','https://www.sanity.io/docs/ai/sanity-context-create-a-knowledge-base'); foreach($u in $urls){ try { $r=Inv...

Agent

▸ exec_command {"cmd":"$r = Invoke-WebRequest -Uri 'https://www.sanity.io/docs/llms.txt' -Us...

Input

$ {"cmd":"$r = Invoke-WebRequest -Uri 'https://www.sanity.io/docs/llms.txt' -UseBasicParsing; $r.Content -split \"`n\" | Where-Object { $_ -match 'Knowledge Base|knowledge-base|sanity-context' } | Selec...

Agent

▸ exec_command {"cmd":"$r = Invoke-WebRequest -Uri 'https://www.sanity.io/docs/llms.txt' -Us...

Input

$ {"cmd":"$r = Invoke-WebRequest -Uri 'https://www.sanity.io/docs/llms.txt' -UseBasicParsing; $r.Content -split \"`n\" | Where-Object { $_ -match 'Knowledge Base|knowledge-base|sanity-context' } | Selec...

Agent

▸ exec_command {"cmd":"$r = Invoke-WebRequest -Uri 'https://www.sanity.io/docs/llms.txt' -Us...

Input

$ {"cmd":"$r = Invoke-WebRequest -Uri 'https://www.sanity.io/docs/llms.txt' -UseBasicParsing; $r.Content.Substring(0,[Math]::Min(5000,$r.Content.Length))","justification":"","login":true,"max_output_tok...

Agent

▸ exec_command {"cmd":"$r = Invoke-WebRequest -Uri 'https://www.sanity.io/docs/llms/ai.txt' ...

Input

$ {"cmd":"$r = Invoke-WebRequest -Uri 'https://www.sanity.io/docs/llms/ai.txt' -UseBasicParsing; $r.Content -split \"`n\" | Where-Object { $_ -match 'Knowledge Base|knowledge-base|Context' } | Select-Ob...

Agent

▸ exec_command {"cmd":"$r = Invoke-WebRequest -Uri 'https://www.sanity.io/docs/llms/ai.txt' ...

Input

$ {"cmd":"$r = Invoke-WebRequest -Uri 'https://www.sanity.io/docs/llms/ai.txt' -UseBasicParsing; $r.Content -split \"`n\" | Where-Object { $_ -match 'Knowledge Base|knowledge-base|Context' } | Select-Ob...

Agent

▸ exec_command {"cmd":"$u='https://www.sanity.io/docs/ai/sanity-context-source-types.md'; $r...

Input

$ {"cmd":"$u='https://www.sanity.io/docs/ai/sanity-context-source-types.md'; $r=Invoke-WebRequest -Uri $u -UseBasicParsing; \"STATUS=$($r.StatusCode) URL=$u\"; $r.Content.Substring(0,[Math]::Min(20000,$...

Agent

▸ exec_command {"cmd":"$u='https://www.sanity.io/docs/ai/sanity-context-create-knowledge-bas...

Input

$ {"cmd":"$u='https://www.sanity.io/docs/ai/sanity-context-create-knowledge-base.md'; $r=Invoke-WebRequest -Uri $u -UseBasicParsing; \"STATUS=$($r.StatusCode) URL=$u\"; $r.Content.Substring(0,[Math]::Mi...

Agent

选择 Website，填写：

https://trac.ffmpeg.org/wiki/Encode

然后点击 Add source。等来源出现在列表中后，点击 Build entries 开始构建。

不要填 https://ffmpeg.org/，整个网站范围太大；这里只需要 FFmpeg 的编码章节。构建结束后把状态截图发我。

正常，Sanity 正在抓取 FFmpeg 编码文档。先不要重复添加来源。

等这一行的 Processing 变成完成状态后，Build entries 按钮会自动变亮。届时点击它开始构建知识条目；如果状态变成 Failed，直接截图发我。

状态是 Ready，但抓到了 0 documents，这等于没有可用内容，不能构建。先不要点 Build entries；我检查一下是 robots.txt 还是页面结构导致的。

Agent

▸ exec_command {"cmd":"$urls=@('https://trac.ffmpeg.org/robots.txt','https://ffmpeg.org/robo...

Input

$ {"cmd":"$urls=@('https://trac.ffmpeg.org/robots.txt','https://ffmpeg.org/robots.txt','https://ffmpeg.org/ffmpeg-codecs.html','https://ffmpeg.org/ffmpeg.html'); foreach($u in $urls){ try { $r=Invoke-We...

Agent

▸ exec_command {"cmd":"$u='https://ffmpeg.org/ffmpeg-codecs.html'; $r=Invoke-WebRequest -Uri...

Input

$ {"cmd":"$u='https://ffmpeg.org/ffmpeg-codecs.html'; $r=Invoke-WebRequest -Uri $u -UseBasicParsing; $text=$r.Content -replace '<script[\\s\\S]*?</script>',' ' -replace '<style[\\s\\S]*?</style>',' ' -r...

Agent

▸ exec_command {"cmd":"$urls=@('https://trac.ffmpeg.org/robots.txt','https://ffmpeg.org/robo...

Input

$ {"cmd":"$urls=@('https://trac.ffmpeg.org/robots.txt','https://ffmpeg.org/robots.txt','https://ffmpeg.org/ffmpeg-codecs.html','https://ffmpeg.org/ffmpeg.html'); foreach($u in $urls){ try { $r=Invoke-We...

Agent

▸ exec_command {"cmd":"$u='https://ffmpeg.org/ffmpeg-codecs.html'; $r=Invoke-WebRequest -Uri...

Input

$ {"cmd":"$u='https://ffmpeg.org/ffmpeg-codecs.html'; $r=Invoke-WebRequest -Uri $u -UseBasicParsing; $text=$r.Content -replace '<script[\\s\\S]*?</script>',' ' -replace '<style[\\s\\S]*?</style>',' ' -r...

Agent

这个来源抓到了 0 篇文档，不能用。把现有来源删掉：

点击该行右侧的三个点。
选择删除来源。
点击 Add source > Website。
填入：

https://ffmpeg.org/ffmpeg-codecs.html

等状态变成 Ready，并确认 Documents 大于 0。
再点击 Build entries。

这是 FFmpeg 的静态编码器文档，比 Wiki 页面更适合被抓取。

正在处理，先等待状态从 Processing 变为 Ready。如果这次 Documents 仍然显示 0，就改用 Files 来源；如果大于 0，直接点击 Build entries。

这个来源太大了：抓取后被拆成 379 个索引文档，而当前方案上限是 150，继续下去会卡住构建。先删掉这个来源，我们不升级套餐，改用单个文件来源。

Agent

▸ exec_command {"cmd":"$dir = 'G:\\视频处理工具\\renderproof\\sources'; New-Item -ItemType Directo...

Input

$ {"cmd":"$dir = 'G:\\视频处理工具\\renderproof\\sources'; New-Item -ItemType Directory -Force -Path $dir | Out-Null; $out = Join-Path $dir 'ffmpeg-codecs.html'; Invoke-WebRequest -Uri 'https://ffmpeg.org/ffm...

Agent

▸ exec_command {"cmd":"$out = 'G:\\视频处理工具\\renderproof\\sources\\ffmpeg-codecs.html'; curl.e...

Input

$ {"cmd":"$out = 'G:\\视频处理工具\\renderproof\\sources\\ffmpeg-codecs.html'; curl.exe -L --retry 3 --retry-delay 2 --fail -o $out 'https://ffmpeg.org/ffmpeg-codecs.html'; if ($LASTEXITCODE -ne 0) { exit $LA...

Agent

当前来源不要继续构建。请先删除它：

点击来源行右侧三个点。
选择 Remove source / Delete source。
等待已用文档数量下降。

然后选择 Add source > Files，上传这个本地文件：

G:\视频处理工具\renderproof\sources\ffmpeg-codecs.html

这是从 FFmpeg 官方下载的静态编码器文档，单个文件不会被拆成 379 个网页。上传后等状态变成 Ready，确认文档数低于 150，再点击 Build entries。

现在正确了：1 document，没有超过 150 的限制。

等状态从 Processing 变成 Ready，然后点击 Build entries。构建有时间会久一些，完成后把 Sources 或 Entries 页面截图发我。

点击 Build entries。

构建过程中不要离开或重复点击。完成后把状态为 Entries up to date 的页面截图发我。

Knowledge Base 已构建成功，目前有 12 entries。

下一步先点击左侧的 Issues，检查 Sanity 是否发现了来源冲突或结构问题。把 Issues 页面截图发我；如果没有问题，我们就创建 MCP Endpoint。

没有冲突，Knowledge Base 当前状态正常。

现在点击左侧 MCP ENDPOINTS 下的 New endpoint。下一页会配置 Agent 读取这个 Knowledge Base，先打开表单并截图发我。

填写：

Title

RenderProof Context

Name

renderproof-context

Instructions

You are RenderProof, a video delivery planning agent. Use the Knowledge Base as the source of truth for codec compatibility, container rules, platform delivery limits, and FFmpeg options. Always cite the entry and original source used. If evidence is missing or sources conflict, explain the uncertainty and request a decision instead of inventing parameters. Generate FFmpeg commands only from verified constraints. Prefer compatibility over compression efficiency unless the user explicitly chooses otherwise.

然后在 Content source 中勾选 RenderProof Video Delivery，点击 Create endpoint。

MCP Endpoint 已创建成功。现在点击右上方的 Connect agent，它会显示 Agent 需要的连接方式和鉴权步骤。把弹窗截图发我。

点击 Copy setup prompt，然后把复制到的完整文字直接粘贴到聊天里。

它通常只包含 Endpoint URL 和初始化说明，不包含 API Token，所以可以粘贴。我会根据这个提示开始搭建项目。

我按这个 MCP 提示开始搭建。先检查当前 Node 环境和最新依赖版本，然后在 renderproof 下创建全新的 Next.js 项目；Token 只会放在本地 .env.local，不会进聊天或仓库。

Agent

▸ exec_command {"cmd":"node --version; npm --version; git --version; Get-ChildItem -Force -R...

Input

$ {"cmd":"node --version; npm --version; git --version; Get-ChildItem -Force -Recurse -Depth 2 | Select-Object FullName,Length,LastWriteTime","justification":"","login":true,"max_output_tokens":12000,"p...

Agent

▸ exec_command {"cmd":"npm info next version; npm info ai version; npm info @ai-sdk/mcp vers...

Input

$ {"cmd":"npm info next version; npm info ai version; npm info @ai-sdk/mcp version; npm info @ai-sdk/deepseek version; npm info @sanity/client version; npm info sanity version","justification":"","login...

Agent

▸ exec_command {"cmd":"Get-ChildItem -Force -Recurse -Depth 2 | Select-Object FullName,Lengt...

Input

$ {"cmd":"Get-ChildItem -Force -Recurse -Depth 2 | Select-Object FullName,Length,LastWriteTime","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_defa...

Agent

▸ write_stdin {"chars":"","max_output_tokens":5000,"session_id":5283,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 5000,
  "session_id": 5283,
  "yield_time_ms": 30000
}

Agent

▸ exec_command {"cmd":"node --version; npm --version; git --version; Get-ChildItem -Force -R...

Input

$ {"cmd":"node --version; npm --version; git --version; Get-ChildItem -Force -Recurse -Depth 2 | Select-Object FullName,Length,LastWriteTime","justification":"","login":true,"max_output_tokens":12000,"p...

Agent

依赖版本已确认。接下来创建独立的 web Next.js 项目，使用 TypeScript、App Router 和 Tailwind；不会复用现有视频工具代码。

Agent

▸ exec_command {"cmd":"npx create-next-app@latest --help","justification":"","login":true,"m...

Input

$ {"cmd":"npx create-next-app@latest --help","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":false,"workdir":"G:...

Agent

▸ exec_command {"cmd":"npx create-next-app@latest web --ts --tailwind --eslint --app --src-d...

Input

$ {"cmd":"npx create-next-app@latest web --ts --tailwind --eslint --app --src-dir --import-alias \"@/*\" --use-npm --empty --disable-git --yes","justification":"","login":true,"max_output_tokens":20000,...

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":57657,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 57657,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":57657,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 57657,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":57657,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 57657,
  "yield_time_ms": 30000
}

Agent

Next.js 依赖正在安装，属于正常的首次下载。安装完成后我会先接 MCP 服务端，再做最小聊天界面。

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":57657,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 57657,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":57657,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 57657,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":57657,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 57657,
  "yield_time_ms": 30000
}

Agent

▸ exec_command {"cmd":"Get-Process node,npm -ErrorAction SilentlyContinue | Select-Object Id...

Input

$ {"cmd":"Get-Process node,npm -ErrorAction SilentlyContinue | Select-Object Id,ProcessName,CPU,StartTime,Path; Get-ChildItem -Force -Path 'G:\\视频处理工具\\renderproof\\web' -ErrorAction SilentlyContinue | ...

Agent

▸ exec_command {"cmd":"if (Test-Path 'G:\\视频处理工具\\renderproof\\web\\package.json') { Get-Con...

Input

$ {"cmd":"if (Test-Path 'G:\\视频处理工具\\renderproof\\web\\package.json') { Get-Content 'G:\\视频处理工具\\renderproof\\web\\package.json' }; if (Test-Path 'G:\\视频处理工具\\renderproof\\web\\node_modules') { (Get-Chi...

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":57657,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 57657,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":57657,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 57657,
  "yield_time_ms": 30000
}

Agent

依赖安装卡在无输出状态，我先中止脚手架进程，再单独运行 npm install 以便看到具体错误，避免一直等待。

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":5000,"session_id":57657,"yield_time_ms"...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 5000,
  "session_id": 57657,
  "yield_time_ms": 1000
}

Agent

▸ exec_command {"cmd":"npm config get registry; npm install --no-audit --no-fund --loglevel=...

Input

$ {"cmd":"npm config get registry; npm install --no-audit --no-fund --loglevel=info","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","shell...

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":19085,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 19085,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":19085,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 19085,
  "yield_time_ms": 30000
}

Agent

可以复用电脑上已经安装的 Node.js、npm 和缓存，但不能直接拿另一个现成 Next.js 项目来参赛。

挑战要求作品在 9 月 18 日之后开始开发，所以必须建立一个全新的项目目录。每个 Next.js 项目还需要自己的 package.json、版本锁定和 node_modules，否则以后容易出现全局版本冲突，也无法稳定部署。

现在安装的正是新项目 renderproof/web 所需的本地依赖，不是在重复安装整个电脑环境。当前网络较慢，安装仍在进行；完成后我会继续接 Sanity MCP 和 DeepSeek。

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":19085,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 19085,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":19085,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 19085,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":19085,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 19085,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":19085,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 19085,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":19085,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 19085,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":19085,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 19085,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":19085,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 19085,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":19085,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 19085,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":19085,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 19085,
  "yield_time_ms": 30000
}

Agent

▸ exec_command {"cmd":"Get-ChildItem -Force -Path 'G:\\视频处理工具\\renderproof\\web' | Select-Ob...

Input

$ {"cmd":"Get-ChildItem -Force -Path 'G:\\视频处理工具\\renderproof\\web' | Select-Object Name,Length,LastWriteTime; if (Test-Path 'G:\\视频处理工具\\renderproof\\web\\node_modules') { (Get-ChildItem 'G:\\视频处理工具\\r...

Agent

官方 npm 源在当前网络下过慢，依赖树还没开始落盘。我切到中国可访问的 npm 镜像继续安装，版本仍按已核对的锁定版本解析。

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":5000,"session_id":19085,"yield_time_ms"...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 5000,
  "session_id": 19085,
  "yield_time_ms": 1000
}

Agent

▸ exec_command {"cmd":"npm install --registry=https://registry.npmmirror.com --no-audit --no...

Input

$ {"cmd":"npm install --registry=https://registry.npmmirror.com --no-audit --no-fund --loglevel=info","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"us...

Agent

▸ exec_command {"cmd":"npm install ai@7.0.107 @ai-sdk/mcp@2.0.54 @ai-sdk/deepseek@3.0.49 zod...

Input

$ {"cmd":"npm install ai@7.0.107 @ai-sdk/mcp@2.0.54 @ai-sdk/deepseek@3.0.49 zod lucide-react --registry=https://registry.npmmirror.com --no-audit --no-fund","justification":"","login":true,"max_output_t...

Agent

基础依赖已安装。现在我会读取脚手架文件，然后一次完成：MCP 客户端封装、DeepSeek 流式聊天 API、最小可用界面、环境变量示例和 README。

Agent

▸ exec_command {"cmd":"Get-Content -Raw package.json; Get-Content -Raw tsconfig.json","justi...

Input

$ {"cmd":"Get-Content -Raw package.json; Get-Content -Raw tsconfig.json","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershel...

Agent

▸ exec_command {"cmd":"Get-Content -Raw 'src\\app\\page.tsx'; Get-Content -Raw 'src\\app\\la...

Input

$ {"cmd":"Get-Content -Raw 'src\\app\\page.tsx'; Get-Content -Raw 'src\\app\\layout.tsx'; Get-Content -Raw 'src\\app\\globals.css'","justification":"","login":true,"max_output_tokens":20000,"prefix_rule...

Agent

▸ exec_command {"cmd":"Get-Content -Raw '.gitignore'; Get-Content -Raw 'next.config.ts'; Get...

Input

$ {"cmd":"Get-Content -Raw '.gitignore'; Get-Content -Raw 'next.config.ts'; Get-Content -Raw 'eslint.config.mjs'","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_perm...

Agent

▸ exec_command {"cmd":"rg -n \"createMCPClient|MCPClient\" node_modules/@ai-sdk/mcp -g '*.d....

Input

$ {"cmd":"rg -n \"createMCPClient|MCPClient\" node_modules/@ai-sdk/mcp -g '*.d.ts' -g '*.md' | Select-Object -First 80","justification":"","login":true,"max_output_tokens":15000,"prefix_rule":[],"sandbo...

Agent

▸ exec_command {"cmd":"rg -n \"deepseek|createDeepSeek\" node_modules/@ai-sdk/deepseek -g '*...

Input

$ {"cmd":"rg -n \"deepseek|createDeepSeek\" node_modules/@ai-sdk/deepseek -g '*.d.ts' -g '*.md' | Select-Object -First 80","justification":"","login":true,"max_output_tokens":15000,"prefix_rule":[],"san...

Agent

▸ exec_command {"cmd":"Get-Content -Raw '.gitignore'; Get-Content -Raw 'next.config.ts'; Get...

Input

$ {"cmd":"Get-Content -Raw '.gitignore'; Get-Content -Raw 'next.config.ts'; Get-Content -Raw 'eslint.config.mjs'","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_perm...

Agent

▸ exec_command {"cmd":"rg -n \"createMCPClient|MCPClient\" node_modules/@ai-sdk/mcp -g '*.d....

Input

$ {"cmd":"rg -n \"createMCPClient|MCPClient\" node_modules/@ai-sdk/mcp -g '*.d.ts' -g '*.md' | Select-Object -First 80","justification":"","login":true,"max_output_tokens":15000,"prefix_rule":[],"sandbo...

Agent

▸ exec_command {"cmd":"rg -n \"deepseek|createDeepSeek\" node_modules/@ai-sdk/deepseek -g '*...

Input

$ {"cmd":"rg -n \"deepseek|createDeepSeek\" node_modules/@ai-sdk/deepseek -g '*.d.ts' -g '*.md' | Select-Object -First 80","justification":"","login":true,"max_output_tokens":15000,"prefix_rule":[],"san...

Agent

▸ exec_command {"cmd":"Get-Content -Raw '.gitignore'; Get-Content -Raw 'next.config.ts'; Get...

Input

$ {"cmd":"Get-Content -Raw '.gitignore'; Get-Content -Raw 'next.config.ts'; Get-Content -Raw 'eslint.config.mjs'","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_perm...

Agent

▸ exec_command {"cmd":"Get-Content -Raw 'node_modules\\@ai-sdk\\mcp\\README.md'","justificat...

Input

$ {"cmd":"Get-Content -Raw 'node_modules\\@ai-sdk\\mcp\\README.md'","justification":"","login":true,"max_output_tokens":24000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","t...

Agent

▸ exec_command {"cmd":"rg -n \"isStepCount|stepCountIs\" node_modules/ai/dist/index.d.ts | S...

Input

$ {"cmd":"rg -n \"isStepCount|stepCountIs\" node_modules/ai/dist/index.d.ts | Select-Object -First 40","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"us...

Agent

▸ exec_command {"cmd":"rg -n \"toTextStreamResponse|onEnd\" node_modules/ai/dist/index.d.ts ...

Input

$ {"cmd":"rg -n \"toTextStreamResponse|onEnd\" node_modules/ai/dist/index.d.ts | Select-Object -First 60","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":...

Agent

▸ exec_command {"cmd":"rg -n \"interface DeepSeekProviderSettings|apiKey|baseURL\" node_modu...

Input

$ {"cmd":"rg -n \"interface DeepSeekProviderSettings|apiKey|baseURL\" node_modules/@ai-sdk/deepseek/dist/index.d.ts | Select-Object -First 40","justification":"","login":true,"max_output_tokens":5000,"p...

Agent

▸ exec_command {"cmd":"rg -n \"isStepCount|stepCountIs\" node_modules/ai/dist/index.d.ts | S...

Input

$ {"cmd":"rg -n \"isStepCount|stepCountIs\" node_modules/ai/dist/index.d.ts | Select-Object -First 40","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"us...

Agent

▸ exec_command {"cmd":"rg -n \"toTextStreamResponse|onEnd\" node_modules/ai/dist/index.d.ts ...

Input

$ {"cmd":"rg -n \"toTextStreamResponse|onEnd\" node_modules/ai/dist/index.d.ts | Select-Object -First 60","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":...

Agent

▸ exec_command {"cmd":"rg -n \"interface DeepSeekProviderSettings|apiKey|baseURL\" node_modu...

Input

$ {"cmd":"rg -n \"interface DeepSeekProviderSettings|apiKey|baseURL\" node_modules/@ai-sdk/deepseek/dist/index.d.ts | Select-Object -First 40","justification":"","login":true,"max_output_tokens":5000,"p...

Agent

▸ exec_command {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $start=(...

Input

$ {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $start=(Select-String -Path $p -Pattern '^type BaseToolExecutionEndFields').LineNumber; $lines[($start-1)..($start+45)]","justifica...

Agent

我现在开始写实现：服务端通过 MCP 读 Knowledge Base，DeepSeek 负责多步工具调用，前端提供可实际提问的 RenderProof 工作界面。所有密钥只从服务端环境变量读取。

补丁因为脚手架 README 文本与预期不完全一致而未应用，我不会覆盖错文件。先读取实际内容，再分段提交。

Agent

▸ exec_command {"cmd":"Get-Content -Raw README.md","justification":"","login":true,"max_outp...

Input

$ {"cmd":"Get-Content -Raw README.md","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":false,"workdir":"G:\\视频处理工...

Agent

▸ exec_command {"cmd":"Get-Content -Raw src\\app\\page.tsx; Get-Content -Raw src\\app\\layou...

Input

$ {"cmd":"Get-Content -Raw src\\app\\page.tsx; Get-Content -Raw src\\app\\layout.tsx; Get-Content -Raw src\\app\\globals.css; Get-Content -Raw package.json","justification":"","login":true,"max_output_t...

Agent

第一版代码已经写入。现在进行静态检查和类型检查；发现问题会直接修正，再做生产构建。

Agent

▸ exec_command {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":150...

Input

$ {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":15000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":false,"workdir":"G:\\视频处理工具\\renderproof...

Agent

▸ exec_command {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens"...

Input

$ {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens":15000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":false,"workdir":"G:\\视频处理工具\\renderp...

Agent

▸ exec_command {"cmd":"npm install --package-lock-only --registry=https://registry.npmmirror...

Input

$ {"cmd":"npm install --package-lock-only --registry=https://registry.npmmirror.com --no-audit --no-fund","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":...

Agent

▸ exec_command {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens"...

Input

$ {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens":15000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":false,"workdir":"G:\\视频处理工具\\renderp...

Agent

▸ exec_command {"cmd":"npm run build","justification":"","login":true,"max_output_tokens":20...

Input

$ {"cmd":"npm run build","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":false,"workdir":"G:\\视频处理工具\\renderproo...

Agent

代码已通过类型检查和生产构建。现在只剩两个本地密钥需要你填写；我先创建被 Git 忽略的 .env.local，不会包含任何秘密。

Agent

▸ open_in_codex {"placement":"right","target":{"line":1,"path":"G:\\视频处理工具\\renderproof\\web\...

Input

{
  "placement": "right",
  "target": {
    "line": 1,
    "path": "G:\\视频处理工具\\renderproof\\web\\.env.local",
    "type": "file"
  }
}

Agent

我先启动本地开发服务器并验证页面能正常渲染；聊天端到端测试会等你填完两个密钥后再执行。

Agent

▸ exec_command {"cmd":"npm run dev -- --hostname 127.0.0.1 --port 3000","justification":"","...

Input

$ {"cmd":"npm run dev -- --hostname 127.0.0.1 --port 3000","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":true,...

Agent

▸ exec_command {"cmd":"npx next dev -H 127.0.0.1 -p 3000","justification":"","login":true,"m...

Input

$ {"cmd":"npx next dev -H 127.0.0.1 -p 3000","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":true,"workdir":"G:\...

Agent

▸ exec_command {"cmd":"curl.exe -s -o NUL -w \"STATUS=%{http_code}\\nCONTENT_TYPE=%{content_...

Input

$ {"cmd":"curl.exe -s -o NUL -w \"STATUS=%{http_code}\\nCONTENT_TYPE=%{content_type}\\n\" http://127.0.0.1:3000/; curl.exe -s -X POST http://127.0.0.1:3000/api/chat -H \"Content-Type: application/json\"...

Agent

▸ exec_command {"cmd":"Get-ChildItem -Force | Select-Object Name,Length,LastWriteTime","just...

Input

$ {"cmd":"Get-ChildItem -Force | Select-Object Name,Length,LastWriteTime","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershel...

Agent

▸ exec_command {"cmd":"try { Invoke-RestMethod -Method Post -Uri 'http://127.0.0.1:3000/api/...

Input

$ {"cmd":"try { Invoke-RestMethod -Method Post -Uri 'http://127.0.0.1:3000/api/chat' -ContentType 'application/json' -Body '{\"messages\":[{\"role\":\"user\",\"content\":\"test\"}]}' } catch { $response...

Agent

▸ js {"code":"let tab = await cua.createBrowserTab(\"iab\", \"http://127.0.0.1:300...

Input

{
  "code": "let tab = await cua.createBrowserTab(\"iab\", \"http://127.0.0.1:3000\", { visible: true });",
  "title": "Open RenderProof preview"
}

Agent

▸ js {"code":"await tab.getScreenshot({ fullPage: true });","title":"Capture Rende...

Input

{
  "code": "await tab.getScreenshot({ fullPage: true });",
  "title": "Capture RenderProof preview"
}

Agent

▸ js {"code":"await (await browser.capabilities.get(\"viewport\")).documentation()...

Input

{
  "code": "await (await browser.capabilities.get(\"viewport\")).documentation();",
  "title": "Read viewport controls"
}

Agent

▸ js {"code":"const browser = await cua.getBrowser({ id: \"1\" }); await (await br...

Input

{
  "code": "const browser = await cua.getBrowser({ id: \"1\" }); await (await browser.capabilities.get(\"viewport\")).documentation();",
  "title": "Read viewport controls"
}

Agent

▸ js {"code":"nodeRepl.write(JSON.stringify(await browser.capabilities.list()));",...

Input

{
  "code": "nodeRepl.write(JSON.stringify(await browser.capabilities.list()));",
  "title": "List browser capabilities"
}

Agent

▸ js {"code":"const viewport = await browser.capabilities.get(\"viewport\"); nodeR...

Input

{
  "code": "const viewport = await browser.capabilities.get(\"viewport\"); nodeRepl.write(Object.keys(viewport).join(\", \"));",
  "title": "Inspect viewport API"
}

Agent

▸ js {"code":"nodeRepl.write(JSON.stringify({ set: typeof viewport.set, setSize: t...

Input

{"code":"nodeRepl.write(JSON.stringify({ set: typeof viewport.set, setSize: typeof viewport.setSize, setViewport: typeof viewport.setViewport, reset: typeof viewport.reset }));","title":"Check viewpor...

Agent

▸ js {"code":"await viewport.set({ width: 1440, height: 900 }); await tab.getScree...

Input

{
  "code": "await viewport.set({ width: 1440, height: 900 }); await tab.getScreenshot({ fullPage: true });",
  "title": "Check desktop layout"
}

Agent

▸ js {"code":"nodeRepl.write(JSON.stringify(await viewport.get()));","title":"Read...

Input

{
  "code": "nodeRepl.write(JSON.stringify(await viewport.get()));",
  "title": "Read current viewport"
}

Agent

▸ js {"code":"nodeRepl.write(JSON.stringify(await tab.playwright.evaluate(() => ({...

Input

{"code":"nodeRepl.write(JSON.stringify(await tab.playwright.evaluate(() => ({ innerWidth: window.innerWidth, innerHeight: window.innerHeight, scrollWidth: document.documentElement.scrollWidth, bodyScr...

Agent

▸ js {"code":"await viewport.set({ width: 390, height: 844 }); nodeRepl.write(JSON...

Input

{"code":"await viewport.set({ width: 390, height: 844 }); nodeRepl.write(JSON.stringify(await tab.playwright.evaluate(() => ({ innerWidth: window.innerWidth, innerHeight: window.innerHeight, scrollWid...

Agent

▸ js {"code":"await viewport.reset(); await tab.markDeliverable();","title":"Reset...

Input

{
  "code": "await viewport.reset(); await tab.markDeliverable();",
  "title": "Reset preview viewport"
}

Agent

▸ exec_command {"cmd":"git check-ignore -v .env.local; if ($LASTEXITCODE -ne 0) { 'WARN: .en...

Input

$ {"cmd":"git check-ignore -v .env.local; if ($LASTEXITCODE -ne 0) { 'WARN: .env.local is not ignored' }; git check-ignore -v .env.example; if ($LASTEXITCODE -ne 0) { 'OK: .env.example is not ignored' }...

Agent

本地代码已经稳定，我把 renderproof 初始化成独立 Git 仓库，确保挑战作品与以前的项目完全分开，并确认密钥文件不会被提交。

Agent

▸ exec_command {"cmd":"git init; git -C web check-ignore -v .env.local; if ($LASTEXITCODE -n...

Input

$ {"cmd":"git init; git -C web check-ignore -v .env.local; if ($LASTEXITCODE -ne 0) { 'WARN: .env.local is not ignored' }; git -C web check-ignore -v .env.example; if ($LASTEXITCODE -ne 0) { 'OK: .env.e...

Agent

我已经新建了独立的 renderproof 项目，没有复用以前的 Next.js 代码。当前包含：

Next.js 16 + TypeScript + Tailwind
DeepSeek 模型接入
Sanity Context MCP 客户端
最多 10 步 MCP 工具调用
RenderProof 聊天界面和服务端流式接口
.env.local 密钥隔离

本地页面正在运行：http://[REDACTED]:3000

已经通过 npm run lint、TypeScript 检查和生产构建。桌面和手机尺寸也已验证，没有横向溢出。聊天接口目前在缺少密钥时返回明确的配置错误，这是预期的。

现在只差两个值。右侧已经打开了 .env.local：

打开 Sanity Organization API Tokens，创建 RenderProof Server Token。权限选 Context Viewer，没有该选项就选 Viewer，到期日建议设为 2026 年 10 月 6 日。把 Token 填到：

SANITY_API_READ_TOKEN=

打开 DeepSeek API Keys 创建 API Key，填到：

DEEPSEEK_API_KEY=

不要把两个密钥发到聊天里。填写完成后告诉我，我会重启服务并实际提问测试，确认 DeepSeek 调用了 Sanity MCP，而且回答来自 Knowledge Base。

不是当前这个 Context 页面。这里创建的是 MCP Endpoint，API Token 要在组织管理里创建。

直接打开这个地址：

https://www.sanity.io/organizations/oey86fpw1/api/tokens

然后：

点击 Add API token。
名称填 RenderProof Server Token。
到期时间选择 2026-10-06。
权限选择 Context Viewer；没有该选项就选择 Viewer。
点击 Add token。
Token 只会完整显示一次，把值填写到 .env.local 的 SANITY_API_READ_TOKEN= 后面。
不要把 Token 发到聊天中。

注意不要进入项目里的 API > Tokens，那是项目级 Token。我们需要的是从顶部组织名 Tate 进入的 Organization API Token。

对，就是这里。点击左侧的 Tokens，不要选 Functions。

进入 Tokens 后点击右上角 Add API token，按前面说的填写：

名称：RenderProof Server Token
到期时间：2026 年 10 月 6 日
权限：Context Viewer，没有就选 Viewer

创建后把 Token 填进右侧打开的 .env.local，不要发到聊天里。

在这里勾选：

Expiration：选择自定义日期 2026-10-06。
Organization permissions：勾选 Context。
在 Context 下选择 Viewer，这就是 Context Viewer 权限。
其他权限全部不选。
Project access 不需要添加项目。
点击 Save token。

Token 生成后只显示一次，把它填进右侧 .env.local 的：

SANITY_API_READ_TOKEN=

不要发到聊天里。

密钥已经写入。现在重启本地服务，然后用真实问题做端到端测试；输出里不会打印任何密钥。

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":5000,"session_id":35885,"yield_time_ms"...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 5000,
  "session_id": 35885,
  "yield_time_ms": 1000
}

Agent

▸ exec_command {"cmd":"npx next dev -H 127.0.0.1 -p 3000","justification":"","login":true,"m...

Input

$ {"cmd":"npx next dev -H 127.0.0.1 -p 3000","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":true,"workdir":"G:\...

Agent

▸ exec_command {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'Compare libx264 ...

Input

$ {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'Compare libx264 and libx265 for a 1080p social video. Cite the Knowledge Base source for the important differences.' }) } | ConvertTo-Json ...

Agent

服务仍认为 Token 为空。我只检查 .env.local 的键名和值长度，不显示密钥内容，确认是保存问题还是变量名问题。

Agent

▸ exec_command {"cmd":"Get-Content -LiteralPath '.env.local' | ForEach-Object { if ($_ -matc...

Input

$ {"cmd":"Get-Content -LiteralPath '.env.local' | ForEach-Object { if ($_ -match '^\\s*([^#=]+)=(.*)$') { $name=$matches[1].Trim(); $value=$matches[2].Trim(); \"$name length=$($value.Length)\" } elseif ...

Agent

还没有真正保存进去。我检查了文件，两个变量当前仍然是空的：

SANITY_API_READ_TOKEN length=0
DEEPSEEK_API_KEY length=0

你把密钥写进了右侧的代码评论里，而不是 .env.local 文件正文。请在编辑器中直接填到对应等号后面：

SANITY_API_READ_TOKEN=这里填 Sanity Token
DEEPSEEK_API_KEY=这里填 DeepSeek Key

然后按 Ctrl+S 保存。

另外，这两个密钥已经出现在聊天评论中，属于敏感信息泄露。建议本次测试完成后立即撤销并重新生成，正式版本只使用新密钥。不要再把密钥发到聊天里；保存后只回复“保存好了”。

这是纯文本配置文件，用 记事本 打开最直接。我现在帮你用记事本打开它。

Agent

▸ exec_command {"cmd":"Start-Process -FilePath 'notepad.exe' -ArgumentList 'G:\\视频处理工具\\rend...

Input

$ {"cmd":"Start-Process -FilePath 'notepad.exe' -ArgumentList 'G:\\视频处理工具\\renderproof\\web\\.env.local'","justification":"","login":true,"max_output_tokens":2000,"prefix_rule":[],"sandbox_permissions":...

Agent

已经用记事本打开 .env.local。

你会看到：

SANITY_API_READ_TOKEN=
DEEPSEEK_API_KEY=

直接在这两行等号后面粘贴对应密钥，然后按 Ctrl+S 保存。保存后回复“保存好了”，不要发送密钥内容。

我先确认文件确实保存成功，只检查变量名和长度，不显示密钥内容。

Agent

▸ exec_command {"cmd":"Get-Content -LiteralPath '.env.local' | ForEach-Object { if ($_ -matc...

Input

$ {"cmd":"Get-Content -LiteralPath '.env.local' | ForEach-Object { if ($_ -match '^\\s*([^#=]+)=(.*)$') { $name=$matches[1].Trim(); $value=$matches[2].Trim(); \"$name length=$($value.Length)\" } }","jus...

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":10000,"session_id":50123,"yield_time_ms...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 10000,
  "session_id": 50123,
  "yield_time_ms": 1000
}

Agent

▸ exec_command {"cmd":"npx next dev -H 127.0.0.1 -p 3000","justification":"","login":true,"m...

Input

$ {"cmd":"npx next dev -H 127.0.0.1 -p 3000","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":true,"workdir":"G:\...

Agent

密钥已正确保存。现在执行真实端到端问题，检查模型是否调用 MCP，并返回带来源的 Knowledge Base 回答。

Agent

▸ exec_command {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'Compare libx264 ...

Input

$ {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'Compare libx264 and libx265 for a 1080p social video. Cite the Knowledge Base source for the important differences.' }) } | ConvertTo-Json ...

Agent

端到端回答成功，而且模型明确报告了 Knowledge Base 缺口，没有编造缺失的平台参数。我再读取服务端日志，确认实际调用了哪些 MCP 工具。

Agent

▸ write_stdin {"chars":"","max_output_tokens":12000,"session_id":50933,"yield_time_ms":1000}

Input

{
  "chars": "",
  "max_output_tokens": 12000,
  "session_id": 50933,
  "yield_time_ms": 1000
}

Agent

保存成功，端到端测试已经跑通。

真实问题测试：比较 libx264 和 libx265 的 1080p 社交视频方案。结果是：

API 返回 200
服务端实际调用了 initial_context
随后调用了 knowledge_base_read
DeepSeek 从 Knowledge Base 生成回答
回答包含条目和来源引用
缺少平台限制数据时，模型明确说明证据不足，没有自行编造

服务端日志确认：

[renderproof] MCP tool completed initial_context
[renderproof] MCP tool completed knowledge_base_read
POST /api/chat 200 in 17.0s

本地页面现在可以实际使用：http://[REDACTED]:3000

不过目前还只是可工作的第一版。Knowledge Base 只有 FFmpeg 编码器文档，所以挑战要求的结构化内容还不够强。下一步需要建立 Sanity Studio 和结构化 Dataset，包括：

平台交付限制
编码器与容器兼容规则
HDR、帧率、音频和像素格式约束
来源和 Claims
冲突来源及决策记录
FFmpeg 管线 Recipe
可执行参数验证

另外，刚才两个密钥曾出现在聊天评论中，本地测试可以继续使用，但部署前必须撤销并重新生成。

下一步回复“继续做结构化数据”，我会创建 studio，设计 schema，并把结构化内容接入现有 MCP Agent。

继续。下一步我先创建 Studio 脚手架并确认当前 Sanity CLI 参数，然后设计结构化 schema、导入种子数据，再把 Dataset 加入 Knowledge Base 和 MCP。

Agent

▸ exec_command {"cmd":"npx create-sanity@latest --help","justification":"","login":true,"max...

Input

$ {"cmd":"npx create-sanity@latest --help","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具...

Agent

▸ exec_command {"cmd":"Get-ChildItem -Force | Select-Object Name,Length,LastWriteTime; git s...

Input

$ {"cmd":"Get-ChildItem -Force | Select-Object Name,Length,LastWriteTime; git status --short","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":"use_default...

Agent

▸ exec_command {"cmd":"npm info sanity version; npm info @sanity/vision version","justificat...

Input

$ {"cmd":"npm info sanity version; npm info @sanity/vision version","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":fal...

Agent

▸ write_stdin {"chars":"","max_output_tokens":12000,"session_id":2030,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 12000,
  "session_id": 2030,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":5000,"session_id":2030,"yield_time_ms":...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 5000,
  "session_id": 2030,
  "yield_time_ms": 1000
}

Agent

▸ exec_command {"cmd":"$env:npm_config_registry='https://registry.npmmirror.com'; npx create...

Input

$ {"cmd":"$env:npm_config_registry='https://registry.npmmirror.com'; npx create-sanity@latest --help","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"us...

Agent

▸ exec_command {"cmd":"rg --files -g 'sanity.config.*' -g 'sanity.cli.*' -g 'schemaTypes/**'...

Input

$ {"cmd":"rg --files -g 'sanity.config.*' -g 'sanity.cli.*' -g 'schemaTypes/**' -g 'schema/**' G:\\视频处理工具 | Select-Object -First 100","justification":"","login":true,"max_output_tokens":10000,"prefix_ru...

Agent

▸ exec_command {"cmd":"Get-Content -Raw 'web\\AGENTS.md'; Get-Content -Raw 'web\\CLAUDE.md'"...

Input

$ {"cmd":"Get-Content -Raw 'web\\AGENTS.md'; Get-Content -Raw 'web\\CLAUDE.md'","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"ba...

Agent

▸ exec_command {"cmd":"npm view sanity@6.15.0 dependencies --json --registry=https://registr...

Input

$ {"cmd":"npm view sanity@6.15.0 dependencies --json --registry=https://registry.npmmirror.com | Select-Object -First 200","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"san...

Agent

▸ exec_command {"cmd":"rg --files -g 'sanity.config.*' -g 'sanity.cli.*' -g 'schemaTypes/**'...

Input

$ {"cmd":"rg --files -g 'sanity.config.*' -g 'sanity.cli.*' -g 'schemaTypes/**' -g 'schema/**' G:\\视频处理工具 | Select-Object -First 100","justification":"","login":true,"max_output_tokens":10000,"prefix_ru...

Agent

▸ exec_command {"cmd":"Get-Content -Raw 'web\\AGENTS.md'; Get-Content -Raw 'web\\CLAUDE.md'"...

Input

$ {"cmd":"Get-Content -Raw 'web\\AGENTS.md'; Get-Content -Raw 'web\\CLAUDE.md'","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"po...

Agent

▸ exec_command {"cmd":"npm view sanity@6.15.0 dependencies --json --registry=https://registr...

Input

$ {"cmd":"npm view sanity@6.15.0 dependencies --json --registry=https://registry.npmmirror.com","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_defa...

Agent

▸ exec_command {"cmd":"npm view sanity@6.15.0 peerDependencies --json --registry=https://reg...

Input

$ {"cmd":"npm view sanity@6.15.0 peerDependencies --json --registry=https://registry.npmmirror.com; npm view @sanity/vision@6.15.0 peerDependencies --json --registry=https://registry.npmmirror.com","jus...

Agent

▸ exec_command {"cmd":"npm view create-sanity@6.0.48 dist.tarball --registry=https://registr...

Input

$ {"cmd":"npm view create-sanity@6.0.48 dist.tarball --registry=https://registry.npmmirror.com; npm view @sanity/cli versions --json --registry=https://registry.npmmirror.com | Select-String -Pattern '8...

Agent

我会先建立 Studio 和 schema，不急于导入猜测数据。种子内容只使用已经抓取并核对过的 FFmpeg 官方文档事实，并保留来源定位。

Agent

▸ exec_command {"cmd":"rg -n -i \"nal-hrd|crf_max|x265-stats|b_strategy|aq-mode|vbv-bufsize\...

Input

$ {"cmd":"rg -n -i \"nal-hrd|crf_max|x265-stats|b_strategy|aq-mode|vbv-bufsize\" 'sources\\ffmpeg-codecs.html' | Select-Object -First 80","justification":"","login":true,"max_output_tokens":18000,"prefi...

Agent

▸ exec_command {"cmd":"$u='https://ffmpeg.org/ffmpeg-formats.html'; $r=Invoke-WebRequest -Ur...

Input

$ {"cmd":"$u='https://ffmpeg.org/ffmpeg-formats.html'; $r=Invoke-WebRequest -Uri $u -UseBasicParsing; \"STATUS=$($r.StatusCode) LENGTH=$($r.RawContentLength)\"; $text=$r.Content -replace '<script[\\s\\S...

Agent

▸ exec_command {"cmd":"npm install --registry=https://registry.npmmirror.com --no-audit --no...

Input

$ {"cmd":"npm install --registry=https://registry.npmmirror.com --no-audit --no-fund","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","shel...

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":26366,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 26366,
  "yield_time_ms": 30000
}

Agent

▸ exec_command {"cmd":"npm run typecheck","justification":"","login":true,"max_output_tokens...

Input

$ {"cmd":"npm run typecheck","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\...

Agent

▸ exec_command {"cmd":"npm run build","justification":"","login":true,"max_output_tokens":20...

Input

$ {"cmd":"npm run build","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\stu...

Agent

Studio 已通过构建。现在把已核对的 FFmpeg 事实写成结构化 Claims、Codec、Container、Rule 和 Recipe 种子数据；不会编造平台限制。

Agent

▸ exec_command {"cmd":"rg -n -i \"crf_max|same semantics as x264|nal-hrd|x265-stats|b_strate...

Input

$ {"cmd":"rg -n -i \"crf_max|same semantics as x264|nal-hrd|x265-stats|b_strategy|aq-mode|vbv-bufsize|cbr is not allowed\" 'sources\\ffmpeg-codecs.html' | Select-Object -First 120","justification":"","l...

Agent

▸ exec_command {"cmd":"rg -n -i \"mov, mp4|mov/mp4|fragmentation|faststart|empty_moov|movfla...

Input

$ {"cmd":"rg -n -i \"mov, mp4|mov/mp4|fragmentation|faststart|empty_moov|movflags\" 'sources\\ffmpeg-codecs.html' | Select-Object -First 80","justification":"","login":true,"max_output_tokens":12000,"pr...

Agent

▸ exec_command {"cmd":"rg -n -i \"crf_max|same semantics as x264|nal-hrd|x265-stats|b_strate...

Input

$ {"cmd":"rg -n -i \"crf_max|same semantics as x264|nal-hrd|x265-stats|b_strategy|aq-mode|vbv-bufsize|cbr is not allowed\" 'sources\\ffmpeg-codecs.html' | Select-Object -First 120","justification":"","l...

Agent

▸ exec_command {"cmd":"rg -n -i \"mov, mp4|mov/mp4|fragmentation|faststart|empty_moov|movfla...

Input

$ {"cmd":"rg -n -i \"mov, mp4|mov/mp4|fragmentation|faststart|empty_moov|movflags\" 'sources\\ffmpeg-codecs.html' | Select-Object -First 80","justification":"","login":true,"max_output_tokens":12000,"pr...

Agent

▸ exec_command {"cmd":"$p='sources\\ffmpeg-codecs.html'; $lines=Get-Content $p; $ranges=@(@(...

Input

$ {"cmd":"$p='sources\\ffmpeg-codecs.html'; $lines=Get-Content $p; $ranges=@(@(5988,6020),@(6188,6210),@(6328,6350),@(5900,5922)); foreach($r in $ranges){ \"--- lines $($r[0])-$($r[1]) ---\"; $lines[($r...

Agent

▸ exec_command {"cmd":"$p='sources\\ffmpeg-codecs.html'; $lines=Get-Content $p; $lines[6202....

Input

$ {"cmd":"$p='sources\\ffmpeg-codecs.html'; $lines=Get-Content $p; $lines[6202..6220]","justification":"","login":true,"max_output_tokens":6000,"prefix_rule":[],"sandbox_permissions":"use_default","shel...

Agent

▸ exec_command {"cmd":"$out='G:\\视频处理工具\\renderproof\\sources\\ffmpeg-formats.html'; curl.ex...

Input

$ {"cmd":"$out='G:\\视频处理工具\\renderproof\\sources\\ffmpeg-formats.html'; curl.exe -L --retry 3 --retry-delay 2 --fail -o $out 'https://ffmpeg.org/ffmpeg-formats.html'; if ($LASTEXITCODE -ne 0) { exit $LA...

Agent

▸ exec_command {"cmd":"$out='G:\\视频处理工具\\renderproof\\sources\\ffmpeg-formats.html'; curl.ex...

Input

$ {"cmd":"$out='G:\\视频处理工具\\renderproof\\sources\\ffmpeg-formats.html'; curl.exe -L --retry 3 --retry-delay 2 --fail -o $out 'https://ffmpeg.org/ffmpeg-formats.html'; if ($LASTEXITCODE -ne 0) { exit $LA...

Agent

▸ exec_command {"cmd":"rg -n -i \"same semantics as x264|constant quality mode|Set the quali...

Input

$ {"cmd":"rg -n -i \"same semantics as x264|constant quality mode|Set the quality for constant quality\" 'sources\\ffmpeg-codecs.html' | Select-Object -First 60","justification":"","login":true,"max_out...

Agent

▸ exec_command {"cmd":"rg -n -i \"same semantics as x264|constant quality mode|Set the quali...

Input

$ {"cmd":"rg -n -i \"same semantics as x264|constant quality mode|Set the quality for constant quality\" 'sources\\ffmpeg-codecs.html' | Select-Object -First 60","justification":"","login":true,"max_out...

Agent

▸ exec_command {"cmd":"$lines=Get-Content 'sources\\ffmpeg-codecs.html'; $lines[6296..6312]"...

Input

$ {"cmd":"$lines=Get-Content 'sources\\ffmpeg-codecs.html'; $lines[6296..6312]","justification":"","login":true,"max_output_tokens":6000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"pow...

Agent

▸ exec_command {"cmd":"rg -n -i \"fragmentation|faststart|movflags|empty_moov\" 'sources\\ff...

Input

$ {"cmd":"rg -n -i \"fragmentation|faststart|movflags|empty_moov\" 'sources\\ffmpeg-formats.html' | Select-Object -First 100","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"...

Agent

▸ exec_command {"cmd":"rg -n -i \"fragmentation|faststart|movflags|empty_moov\" 'sources\\ff...

Input

$ {"cmd":"rg -n -i \"fragmentation|faststart|movflags|empty_moov\" 'sources\\ffmpeg-formats.html' | Select-Object -First 100","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"...

Agent

▸ exec_command {"cmd":"$lineNo=0; $errors=0; Get-Content 'seed\\seed.ndjson' | ForEach-Objec...

Input

$ {"cmd":"$lineNo=0; $errors=0; Get-Content 'seed\\seed.ndjson' | ForEach-Object { $lineNo++; if ($_ -notmatch '^\\s*$') { try { $_ | ConvertFrom-Json -AsHashtable | Out-Null } catch { $errors++; \"LINE...

Agent

种子数据已通过 JSON 校验，共 26 条结构化文档。下一步需要让 Sanity CLI 登录你的账号，然后导入 Dataset 并部署 schema。

Agent

▸ exec_command {"cmd":"npx sanity login --help; npx sanity dataset import --help; npx sanity...

Input

$ {"cmd":"npx sanity login --help; npx sanity dataset import --help; npx sanity schema deploy --help","justification":"","login":true,"max_output_tokens":16000,"prefix_rule":[],"sandbox_permissions":"us...

Agent

▸ exec_command {"cmd":"npx sanity login --open","justification":"","login":true,"max_output_...

Input

$ {"cmd":"npx sanity login --open","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":true,"workdir":"G:\\视频处理工具\\r...

Agent

Sanity CLI 需要选择登录方式。你注册或登录 Sanity 时用的是哪种？

Google
GitHub
邮箱链接

回复其中一个，我继续启动对应登录。

Agent

▸ exec_command {"cmd":"npx sanity login --provider google --open","justification":"","login"...

Input

$ {"cmd":"npx sanity login --provider google --open","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":true,"workd...

Agent

浏览器已打开 Sanity 的 Google 登录页。请你在该窗口完成登录；我会等待 CLI 收到授权后自动继续导入和部署。

Agent

▸ write_stdin {"chars":"","max_output_tokens":12000,"session_id":86488,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 12000,
  "session_id": 86488,
  "yield_time_ms": 30000
}

Agent

登录成功。现在部署 Studio schema，然后导入 26 条结构化文档。

Agent

▸ exec_command {"cmd":"npx sanity schemas deploy --verbose","justification":"","login":true,...

Input

$ {"cmd":"npx sanity schemas deploy --verbose","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":false,"workdir":"...

Agent

▸ exec_command {"cmd":"npx sanity datasets import seed\\seed.ndjson --dataset production --r...

Input

$ {"cmd":"npx sanity datasets import seed\\seed.ndjson --dataset production --replace","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","she...

Agent

结构化数据已经成功导入 Sanity Dataset。接下来把 Dataset 和 FFmpeg Formats 文档加入现有 Knowledge Base，再重新构建，让 Agent 同时读取结构化规则和文档证据。

Agent

▸ js {"code":"await cua.getState({ emit: false }).then((state) => nodeRepl.write(J...

Input

{
  "code": "await cua.getState({ emit: false }).then((state) => nodeRepl.write(JSON.stringify(state)));",
  "title": "Check available browsers"
}

Agent

结构化数据已经导入成功：26 documents。现在把 Dataset 接入现有 Knowledge Base。

进入：

Context > RenderProof Video Delivery > Sources

依次操作：

点击 Add source > Dataset。
Project 选择 RenderProof。
Dataset 选择 production。
Query 填：

*[_type in ["sourceDocument","sourceClaim","codecProfile","containerProfile","compatibilityRule","deliveryProfile","pipelineRecipe","decision"]]

点击 Add source。
再点击 Add source > Files，上传：

G:\视频处理工具\renderproof\sources\ffmpeg-formats.html

等两个来源都变成 Ready，点击 Build entries。
构建完成后截图给我。

这样 Knowledge Base 会同时包含结构化规则、来源 Claims 和 FFmpeg 的格式文档。

点击左侧栏的 Context。

进入后选择 RenderProof Video Delivery，再点击 Sources，继续添加 Dataset 和 Files 来源。

点击上面的 GROQ query 标签，然后粘贴：

*[_type in ["sourceDocument","sourceClaim","codecProfile","containerProfile","compatibilityRule","deliveryProfile","pipelineRecipe","decision"]]

确认预览能读取出文档后，点击 Add source。然后再添加 ffmpeg-formats.html 文件来源。

先点击 Preview，确认能读取到 26 条文档。确认无误后再点击 Add source。

如果 Preview 没有结果，就把完整查询重新替换一遍；输入框横向滚动不会影响完整内容。

Preview 正确读取到 26 documents。点击 Add source。

添加完成后，再点击 Add source > Files，上传：

G:\视频处理工具\renderproof\sources\ffmpeg-formats.html

来源已全部接入，但界面当前没有直接显示 Build entries。我核对一下增量重建操作，避免点错。

Agent

▸ exec_command {"cmd":"$r=Invoke-WebRequest -Uri 'https://www.sanity.io/docs/ai/sanity-conte...

Input

$ {"cmd":"$r=Invoke-WebRequest -Uri 'https://www.sanity.io/docs/ai/sanity-context-maintain-knowledge-base.md' -UseBasicParsing; $r.Content.Substring(0,[Math]::Min(20000,$r.Content.Length))","justificati...

Agent

三个来源都已 Ready。现在等 Check for changes 按钮变为可点击，然后点击它。

它会生成新增结构化内容的 Issues，不需要重新做全量 Build。完成后进入左侧的 Issues，把页面截图发我。

点击 Check for changes。

完成后进入左侧 Issues，查看新增来源是否产生冲突或结构问题，然后把 Issues 页面截图发我。

Issues 为 0，说明没有需要解决的冲突。

点击左侧 Entries，确认结构化 Dataset 是否已经产生新的条目，并把页面截图发我。

结构化数据还没有进入当前 Entries，现有大纲仍然主要来自 FFmpeg 文档。点击右上角的 Rebuild，让 Knowledge Base 重新读取全部三个来源。

Rebuild 完成后回到 Issues；如果出现冲突，我们逐项处理。

点击 Cancel，这个按钮只重建当前“Audio Codec Reference”页面，不会完整导入新来源。

然后点击顶部面包屑里的 RenderProof Video Delivery，进入 Knowledge Base 总览。那里才应该有全量 Rebuild 选项，截图给我确认。

现在按钮是可用的。点击 Check for changes，然后等页面顶部出现处理状态或按钮再次变为可用。

处理完成后不要再点 Entries，先看这个黄色的 Sources changed since the last build 是否消失，并截图给我。

黄色提示本身可能可以点开。请点击 Sources changed since the last build 这段文字，看看是否出现变更详情或 Rebuild 操作，然后把页面截图发我。

已经找到了：32 个新来源正在等待首次构建。向下滚动这个弹窗到底部，底部应该有 Apply changes、Build 或类似按钮。滚到底后截图给我确认。

先关闭这个变更列表弹窗，不要删除任何内容。关闭后看页面顶部是否变成 Changes detected、Review 或其他操作入口；把页面截图发我。

这个页面确实没有全量重建入口。点击 Settings，看看 Knowledge Base 设置里是否有 Rebuild、Build entries 或删除/重建选项，先截图不要修改。

这里就是入口。点击底部 Rebuild knowledge base。

它会用当前三个来源重新生成全部条目。完成后先检查 Issues，没有阻塞问题就重新测试 RenderProof 的问答。

点击 Rebuild。

构建期间不要重复点击。完成后回到 Issues，有冲突就截图；没有问题就直接告诉我，我会重新运行真实问题测试。

重建是后台任务，没有弹窗成功提示。可以看这几个信号：

Sources 顶部黄色的 Sources changed since the last build 消失。
Settings > Activity 里的 Last built 从 1小时前 变成 just now。
Entries 页面出现新的结构化主题，例如 Compatibility Rules、Pipeline Recipes、Source Claims。
重建期间按钮可能变成处理中或暂时不可用。

现在等待并偶尔刷新页面。完成后进入 Settings 看 Last built，把 Activity 区域截图发我。

重建完成：现在 14 entries、Issues 0。接下来用结构化 Dataset 才能回答的问题做第二轮端到端测试，例如 libx264 nal-hrd=cbr 与 MP4 的冲突。

Agent

▸ exec_command {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'Can I use libx26...

Input

$ {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'Can I use libx264 nal-hrd=cbr when the output container is MP4? What other HRD requirement applies, and which structured claims or rules su...

Agent

▸ write_stdin {"chars":"","max_output_tokens":12000,"session_id":50933,"yield_time_ms":1000}

Input

{
  "chars": "",
  "max_output_tokens": 12000,
  "session_id": 50933,
  "yield_time_ms": 1000
}

Agent

第二次测试成功，回答已经引用了 Dataset 中的规则、Claims、Delivery Profiles 和 Recipes。现在把界面的来源信息从旧的 12 条更新为真实的 14 条结构化条目。

Agent

▸ exec_command {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":120...

Input

$ {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\web"...

Agent

▸ exec_command {"cmd":"npm run build","justification":"","login":true,"max_output_tokens":20...

Input

$ {"cmd":"npm run build","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\web...

Agent

▸ exec_command {"cmd":"npm run typecheck","justification":"","login":true,"max_output_tokens...

Input

$ {"cmd":"npm run typecheck","justification":"","login":true,"max_output_tokens":15000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\...

Agent

▸ js {"code":"await tab.reload(); nodeRepl.write(await tab.playwright.locator('asi...

Input

{
  "code": "await tab.reload(); nodeRepl.write(await tab.playwright.locator('aside').innerText({ timeoutMs: 10000 }));",
  "title": "Verify updated preview"
}

Agent

▸ js {"code":"await tab.playwright.getByRole('button', { name: 'What constraints s...

Input

{"code":"await tab.playwright.getByRole('button', { name: 'What constraints should I verify before delivering an H.264 MP4?' }).click(); const answer = await tab.playwright.evaluate(async () => { for ...

Agent

▸ js {"code":"await tab.getAXState();","title":"Check chat state"}

Input

{
  "code": "await tab.getAXState();",
  "title": "Check chat state"
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":12000,"session_id":50933,"yield_time_ms":1000}

Input

{
  "chars": "",
  "max_output_tokens": 12000,
  "session_id": 50933,
  "yield_time_ms": 1000
}

Agent

结构化数据已接入并有第二轮成功测试。现在补上最关键的可视化能力：把实际调用的 MCP 工具流式显示在回答旁边，让评委能直接看到 Agent 正在读取 Knowledge Base。

Agent

▸ exec_command {"cmd":"rg -n \"type TextStreamPart|text-delta|tool-call|tool-result\" node_m...

Input

$ {"cmd":"rg -n \"type TextStreamPart|text-delta|tool-call|tool-result\" node_modules/ai/dist/index.d.ts | Select-Object -First 120","justification":"","login":true,"max_output_tokens":16000,"prefix_rul...

Agent

▸ exec_command {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $lines[1...

Input

$ {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $lines[1110..1330]","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default",...

Agent

▸ exec_command {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $lines[1...

Input

$ {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $lines[1110..1330]","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default",...

Agent

▸ exec_command {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $lines[2...

Input

$ {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $lines[2920..3010]","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default",...

Agent

▸ exec_command {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $lines[2...

Input

$ {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $lines[2338..2362]","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"use_default","...

Agent

▸ exec_command {"cmd":"rg -n \"type TextStreamTextDeltaPart\" node_modules/ai/dist/index.d.t...

Input

$ {"cmd":"rg -n \"type TextStreamTextDeltaPart\" node_modules/ai/dist/index.d.ts","justification":"","login":true,"max_output_tokens":2000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"p...

Agent

▸ exec_command {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $lines[2...

Input

$ {"cmd":"$p='node_modules/ai/dist/index.d.ts'; $lines=Get-Content $p; $lines[2900..2920]","justification":"","login":true,"max_output_tokens":4000,"prefix_rule":[],"sandbox_permissions":"use_default","...

Agent

▸ exec_command {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":150...

Input

$ {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":15000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\web"...

Agent

▸ exec_command {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens"...

Input

$ {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens":15000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\...

Agent

▸ exec_command {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'What does libx26...

Input

$ {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'What does libx264 crf_max do? Cite the structured claim.' }) } | ConvertTo-Json -Depth 5; $r = Invoke-WebRequest -Method Post -Uri 'http://...

Agent

NDJSON 测试命令的解析方式不对，我直接查看响应头和开头几行，确认服务端流格式，不涉及代码回退。

Agent

▸ exec_command {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'Define crf for l...

Input

$ {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'Define crf for libx264 in one sentence.' }) } | ConvertTo-Json -Depth 5; $r = Invoke-WebRequest -Method Post -Uri 'http://127.0.0.1:3000/ap...

Agent

▸ exec_command {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'Define crf for l...

Input

$ {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'Define crf for libx264 in one sentence.' }) } | ConvertTo-Json -Depth 5; $r = Invoke-WebRequest -Method Post -Uri 'http://127.0.0.1:3000/ap...

Agent

▸ js {"code":"await tab.reload(); await tab.playwright.getByRole('button', { name:...

Input

{"code":"await tab.reload(); await tab.playwright.getByRole('button', { name: 'Compare libx264 and libx265 for a 1080p social video.' }).click(); await tab.playwright.getByText('Knowledge base', { exa...

Agent

▸ js {"code":"await tab.getAXState();","title":"Check UI after prompt click"}

Input

{
  "code": "await tab.getAXState();",
  "title": "Check UI after prompt click"
}

Agent

▸ js {"code":"await tab.getAXState();","title":"Check completed tool trace"}

Input

{
  "code": "await tab.getAXState();",
  "title": "Check completed tool trace"
}

Agent

第二轮回答暴露出一个真实问题：模型虽然引用了结构化规则，但擅自补出了 Knowledge Base 没有验证的 MPEG-TS 备用容器。我要把“当前证据没有建立备用容器”本身建模成 Claim、Rule 和 Decision，再次导入并重建，收紧这个漏洞。

Agent

▸ exec_command {"cmd":"$lineNo=0; $errors=0; Get-Content 'seed\\seed.ndjson' | ForEach-Objec...

Input

$ {"cmd":"$lineNo=0; $errors=0; Get-Content 'seed\\seed.ndjson' | ForEach-Object { $lineNo++; if ($_ -notmatch '^\\s*$') { try { $_ | ConvertFrom-Json -AsHashtable | Out-Null } catch { $errors++; \"LINE...

Agent

▸ exec_command {"cmd":"npx sanity datasets import seed\\seed.ndjson --dataset production --r...

Input

$ {"cmd":"npx sanity datasets import seed\\seed.ndjson --dataset production --replace","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","she...

Agent

▸ exec_command {"cmd":"npx sanity datasets import 'seed\\seed.ndjson' --dataset production -...

Input

$ {"cmd":"npx sanity datasets import 'seed\\seed.ndjson' --dataset production --replace","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","s...

Agent

补充内容已导入，现在是 29 条结构化文档。请再执行一次全量重建：

Context > RenderProof Video Delivery > Settings > Rebuild knowledge base

重建完成后回到 Sources，确认顶部显示 Entries up to date，黄色变更提示消失。然后我会重新测试，确认 Agent 不再擅自推荐未验证的 MPEG-TS。

好，今天到这里。我先停掉本地开发服务器，避免后台继续占用资源。

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":5000,"session_id":50933,"yield_time_ms"...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 5000,
  "session_id": 50933,
  "yield_time_ms": 1000
}

Agent

停止日志里暴露了一个热更新期间的旧消息兼容问题：旧消息没有 tools 字段时会报错。我今天顺手把这个边界修掉，不影响你明天继续。

Agent

▸ exec_command {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":100...

Input

$ {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\web"...

Agent

▸ exec_command {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens"...

Input

$ {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\...

Agent

今天收工，开发服务器已经停止。

当前进度：

独立仓库：G:\视频处理工具\renderproof
Next.js 应用与 Sanity Studio 均已建立
Sanity Dataset production 已导入 29 条结构化文档
包含 Claims、Codec、Container、Compatibility Rules、Delivery Profiles、Recipes 和 Decisions
Agent 已通过真实端到端测试
界面能流式显示 initial_context、knowledge_base_read 等 MCP 工具调用
Lint、TypeScript 检查和生产构建均通过

明天继续时只需要先做：

Context > RenderProof Video Delivery > Settings > Rebuild knowledge base

这是为了把最后补充的“不得擅自推荐未经证实的 MPEG-TS 备用容器”规则纳入 Knowledge Base。重建后我会重新测试，然后继续完善交付体验、部署和 DEV 参赛文章。

另外，.env.local 中的两个密钥曾在聊天评论里暴露，正式部署前需要撤销并重新生成。今天本地使用不受影响。

开工。今天先把 Knowledge Base 增量内容完成重建并复测，然后继续做部署和参赛材料。我先恢复本地服务并检查昨天的代码状态。

Agent

▸ exec_command {"cmd":"git status --short; Get-ChildItem -Force | Select-Object Name,Length,...

Input

$ {"cmd":"git status --short; Get-ChildItem -Force | Select-Object Name,Length,LastWriteTime","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_defaul...

Agent

▸ exec_command {"cmd":"npm run lint; npx tsc --noEmit","justification":"","login":true,"max_...

Input

$ {"cmd":"npm run lint; npx tsc --noEmit","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\...

Agent

▸ exec_command {"cmd":"npm run typecheck; npm run build","justification":"","login":true,"ma...

Input

$ {"cmd":"npm run typecheck; npm run build","justification":"","login":true,"max_output_tokens":16000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工...

Agent

▸ exec_command {"cmd":"npx next dev -H 127.0.0.1 -p 3000","justification":"","login":true,"m...

Input

$ {"cmd":"npx next dev -H 127.0.0.1 -p 3000","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":true,"workdir":"G:\\视频处理工...

Agent

请在浏览器里完成今天的阻塞步骤：Context > RenderProof Video Delivery > Settings > Rebuild knowledge base，点击 Rebuild 并等待 Entries up to date。

你操作期间，我会补齐仓库文档和自动烟雾测试，用于验证 MCP 工具调用、结构化引用，以及回答中不能出现未经验证的 MPEG-TS 建议。

你重建 Knowledge Base 时，我继续修一个明显的界面问题：当前回答会把 Markdown 表格和加粗符号原样显示。我要接入正式 Markdown 渲染，让引用、命令和对比表更适合评审展示。

Agent

▸ exec_command {"cmd":"npm info react-markdown version --registry=https://registry.npmmirror...

Input

$ {"cmd":"npm info react-markdown version --registry=https://registry.npmmirror.com","justification":"","login":true,"max_output_tokens":4000,"prefix_rule":[],"sandbox_permissions":"use_default","shell"...

Agent

▸ exec_command {"cmd":"npm info remark-gfm version --registry=https://registry.npmmirror.com...

Input

$ {"cmd":"npm info remark-gfm version --registry=https://registry.npmmirror.com","justification":"","login":true,"max_output_tokens":4000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"ba...

Agent

▸ exec_command {"cmd":"npm install react-markdown@10.1.0 remark-gfm@4.0.1 --registry=https:/...

Input

$ {"cmd":"npm install react-markdown@10.1.0 remark-gfm@4.0.1 --registry=https://registry.npmmirror.com --no-audit --no-fund","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"s...

Agent

▸ exec_command {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":120...

Input

$ {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\web"...

Agent

▸ exec_command {"cmd":"npm run build","justification":"","login":true,"max_output_tokens":18...

Input

$ {"cmd":"npm run build","justification":"","login":true,"max_output_tokens":18000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\web...

Agent

今天的开发进度已推进：

补齐根目录 README、挑战合规清单和演示脚本
新增自动烟雾测试 npm run smoke
接入正式 Markdown 渲染，表格、引用和 FFmpeg 命令不再显示原始符号
Web 的 Lint、生产构建和 Studio 构建均通过
本地服务正在运行：http://[REDACTED]:3000

现在需要你完成唯一的手动阻塞步骤：

Context > RenderProof Video Delivery > Settings > Rebuild knowledge base

点击 Rebuild，等待黄色变更提示消失，并确认 Entries up to date。完成后回复“重建好了”，我会立即运行自动测试，重点验证 Agent 不再推荐未经证实的 MPEG-TS 容器。

截图里 Dataset 仍显示 26 documents，说明刚补充的 3 条还没有进入 Dataset Source 快照；这次重建用的仍是旧版本。

请再操作一次：

点击 Check for changes。
等待 Dataset 更新为 29 documents。
黄色变更提示出现后，进入 Settings > Rebuild knowledge base。
再点击 Rebuild，等待 Entries up to date。

这次重建后应显示 15 entries，并且新规则才真正生效。完成后回复“好了”。

出现了预期的核心冲突：一个来源建议使用 MKV/MPEG-TS，另一个结构化 Decision 明确说当前证据没有建立备用容器。这正是我们要展示的 Knowledge Base 冲突处理能力。

点击这条 H.264 (libx264) Encoding ... / Conflict，查看并列来源，然后截图。我们要选择结构化、已验证的 claim 作为 ground truth。

选择第二个结论：

The indexed FFmpeg section that prohibits libx264 nal-hrd=cbr in MP4 does not name a replacement container. No fallback container is established...

然后点击 Resolve issue。

第一个结论引入了未经当前证据验证的 MKV/MPEG-TS 推荐，不应该成为 ground truth。

冲突已按结构化 Decision 解决，并保存成未来重建时沿用的 Instruction。现在运行自动烟雾测试，直接验证 Agent 是否还会漂移到未经验证的 MPEG-TS。

Agent

▸ exec_command {"cmd":"npm run smoke","justification":"","login":true,"max_output_tokens":20...

Input

$ {"cmd":"npm run smoke","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\web...

Agent

核心行为已经通过自动测试，且修复了 Knowledge Base 冲突。现在整理仓库：确认密钥未进入 Git、检查待提交文件，然后建立第一个稳定提交。

Agent

▸ exec_command {"cmd":"git status --short --ignored","justification":"","login":true,"max_ou...

Input

$ {"cmd":"git status --short --ignored","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\r...

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":73140,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 73140,
  "yield_time_ms": 30000
}

Agent

▸ exec_command {"cmd":"git add .; git status --short; git commit -m \"Build RenderProof Sani...

Input

$ {"cmd":"git add .; git status --short; git commit -m \"Build RenderProof Sanity MCP agent\"","justification":"","login":true,"max_output_tokens":30000,"prefix_rule":[],"sandbox_permissions":"use_defau...

Agent

第一次提交包含了一个不该跟踪的 Sanity 运行时目录。我会把生成目录从 Git 中移除，并在 Studio 里加入忽略规则，文件本身不删除。

Agent

▸ exec_command {"cmd":"git rm -r --cached studio/.sanity; git add studio/.gitignore; git com...

Input

$ {"cmd":"git rm -r --cached studio/.sanity; git add studio/.gitignore; git commit -m \"Ignore Sanity runtime output\"","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbo...

Agent

今天的核心里程碑已经完成：

Knowledge Base 现在有 15 entries
结构化 Dataset 为 29 documents
MKV/MPEG-TS 与“无已验证备用容器”的冲突已按结构化 Decision 解决
自动烟雾测试全部通过
MCP 工具调用、结构化引用和拒绝未经证实容器均验证成功
代码已建立两个本地 Git 提交

部署前必须先处理安全问题。之前两个密钥曾出现在聊天评论中，现在是已暴露状态，不能直接部署到 Vercel。

请完成密钥轮换：

在 Sanity Tokens 删除旧的 RenderProof Server Token，重新创建同权限、同有效期的 Token。
在 DeepSeek API Keys 删除旧 Key，创建新 Key。
在记事本的 G:\视频处理工具\renderproof\web\.env.local 中替换两项值，按 Ctrl+S。

不要发送密钥内容。完成后回复“替换好了”，我会重启、重新测试，然后开始部署和参赛文章。

对，配置完全正确：

名称：RenderProof Server Token
到期：2026-10-06
只勾选 Context
Context > Viewer
其他权限不选
Project access 不添加项目

点击 Save token。Token 只会完整显示一次，直接替换到 .env.local 的 SANITY_API_READ_TOKEN= 后并保存，不要发到聊天里。DeepSeek 新 Key 也替换完成后回复“替换好了”。

我先重启服务并重新运行完整烟雾测试，确认新密钥可用且核心行为没有回退。

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":8000,"session_id":41292,"yield_time_ms"...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 8000,
  "session_id": 41292,
  "yield_time_ms": 1000
}

Agent

▸ exec_command {"cmd":"npx next dev -H 127.0.0.1 -p 3000","justification":"","login":true,"m...

Input

$ {"cmd":"npx next dev -H 127.0.0.1 -p 3000","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":true,"workdir":"G:\\视频处理工...

Agent

▸ exec_command {"cmd":"npm run smoke","justification":"","login":true,"max_output_tokens":20...

Input

$ {"cmd":"npm run smoke","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\web...

Agent

新密钥验证通过，四项烟雾测试全部成功。现在开始部署到 Vercel；先确认 CLI 版本和部署参数，再启动登录。

Agent

▸ exec_command {"cmd":"$env:npm_config_registry='https://registry.npmmirror.com'; npx vercel...

Input

$ {"cmd":"$env:npm_config_registry='https://registry.npmmirror.com'; npx vercel@latest --version","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":"use_def...

Agent

▸ exec_command {"cmd":"git status --short; git log --oneline -3","justification":"","login":...

Input

$ {"cmd":"git status --short; git log --oneline -3","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:...

Agent

▸ exec_command {"cmd":"npx vercel@latest login --help","justification":"","login":true,"max_...

Input

$ {"cmd":"npx vercel@latest login --help","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\...

Agent

现在启动 Vercel 登录。浏览器打开后请在页面完成授权；我不会接触你的账号密码。

Agent

▸ exec_command {"cmd":"npx vercel@latest login","justification":"","login":true,"max_output_...

Input

$ {"cmd":"npx vercel@latest login","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":true,"workdir":"G:\\视频处理工具\\renderp...

Agent

▸ exec_command {"cmd":"Start-Process 'https://vercel.com/oauth/device?user_code=ZCCX-KBLQ'",...

Input

$ {"cmd":"Start-Process 'https://vercel.com/oauth/device?user_code=ZCCX-KBLQ'","justification":"","login":true,"max_output_tokens":2000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash...

Agent

▸ exec_command {"cmd":"Start-Process 'https://vercel.com/oauth/device?user_code=ZCCX-KBLQ'",...

Input

$ {"cmd":"Start-Process 'https://vercel.com/oauth/device?user_code=ZCCX-KBLQ'","justification":"","login":true,"max_output_tokens":2000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powe...

Agent

请在浏览器打开 Vercel 授权页面：

https://vercel.com/oauth/device?user_code=ZCCX-KBLQ

完成登录和授权后告诉我。CLI 正在等待认证。

这是 Vercel 新账号的手机验证页。手机号和验证码都由你本人输入，不要发给我。

如果你愿意用手机号验证，就按页面流程完成；成功后告诉我。若不想使用 Vercel，我停止这次登录并改用 Cloudflare 部署，不过需要你已有 Cloudflare 账号。

好，改用 Cloudflare，不创建 Vercel 账号。我先停止等待中的 Vercel 登录，然后按 Cloudflare 的当前部署规范处理。

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":3000,"session_id":56951,"yield_time_ms"...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 3000,
  "session_id": 56951,
  "yield_time_ms": 1000
}

Agent

▸ exec_command {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\cloudflar...

Input

$ {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\cloudflare\\SKILL.md'","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default...

Agent

▸ exec_command {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\wrangler\...

Input

$ {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\wrangler\\SKILL.md'","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default",...

Agent

▸ exec_command {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\workers-b...

Input

$ {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\workers-best-practices\\SKILL.md'","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":...

Agent

▸ exec_command {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\cloudflar...

Input

$ {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\cloudflare\\SKILL.md'","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default...

Agent

▸ exec_command {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\wrangler\...

Input

$ {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\wrangler\\SKILL.md'","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default",...

Agent

▸ exec_command {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\workers-b...

Input

$ {"cmd":"Get-Content -Raw 'C:\\Users\\Administrator\\.codex\\skills\\workers-best-practices\\SKILL.md'","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":...

Agent

Cloudflare 账号已有，我们用 Cloudflare Workers 部署 Next.js，不使用 Vercel。先读取 Cloudflare 最新的 Next.js 框架部署文档，避免使用过时的 Pages 方案。

Agent

▸ exec_command {"cmd":"$r=Invoke-WebRequest -Uri 'https://developers.cloudflare.com/workers/...

Input

$ {"cmd":"$r=Invoke-WebRequest -Uri 'https://developers.cloudflare.com/workers/framework-guides/web-apps/nextjs/' -UseBasicParsing; \"STATUS=$($r.StatusCode) LENGTH=$($r.RawContentLength)\"; $t=$r.Conte...

Agent

▸ exec_command {"cmd":"$r=Invoke-WebRequest -Uri 'https://developers.cloudflare.com/workers/...

Input

$ {"cmd":"$r=Invoke-WebRequest -Uri 'https://developers.cloudflare.com/workers/best-practices/workers-best-practices/' -UseBasicParsing; \"STATUS=$($r.StatusCode) LENGTH=$($r.RawContentLength)\"; $t=$r....

Agent

▸ exec_command {"cmd":"$r=Invoke-WebRequest -Uri 'https://developers.cloudflare.com/workers/...

Input

$ {"cmd":"$r=Invoke-WebRequest -Uri 'https://developers.cloudflare.com/workers/framework-guides/web-apps/nextjs/' -UseBasicParsing; \"STATUS=$($r.StatusCode) LENGTH=$($r.RawContentLength)\"; $t=$r.Conte...

Agent

▸ exec_command {"cmd":"$r=Invoke-WebRequest -Uri 'https://developers.cloudflare.com/workers/...

Input

$ {"cmd":"$r=Invoke-WebRequest -Uri 'https://developers.cloudflare.com/workers/best-practices/workers-best-practices/' -UseBasicParsing; \"STATUS=$($r.StatusCode) LENGTH=$($r.RawContentLength)\"; $t=$r....

Agent

Cloudflare 现在推荐用 vinext 部署 Next.js 16，OpenNext 仅作为兼容性回退。我先运行兼容性检查，再决定迁移方式。

Agent

▸ exec_command {"cmd":"$env:npm_config_registry='https://registry.npmmirror.com'; npx vinext...

Input

$ {"cmd":"$env:npm_config_registry='https://registry.npmmirror.com'; npx vinext check","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","she...

Agent

▸ exec_command {"cmd":"$env:npm_config_registry='https://registry.npmmirror.com'; npx vinext...

Input

$ {"cmd":"$env:npm_config_registry='https://registry.npmmirror.com'; npx vinext init --help","justification":"","login":true,"max_output_tokens":16000,"prefix_rule":[],"sandbox_permissions":"use_default...

Agent

▸ exec_command {"cmd":"npm view vinext version --registry=https://registry.npmmirror.com; np...

Input

$ {"cmd":"npm view vinext version --registry=https://registry.npmmirror.com; npm view @vinext/cloudflare version --registry=https://registry.npmmirror.com","justification":"","login":true,"max_output_to...

Agent

▸ write_stdin {"chars":"","max_output_tokens":20000,"session_id":26398,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 20000,
  "session_id": 26398,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":16000,"session_id":14148,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 16000,
  "session_id": 14148,
  "yield_time_ms": 30000
}

Agent

兼容性检查通过，只有 package.json 缺少 Vite 所需的 ESM 标记，vinext init 会自动处理。现在执行 Cloudflare Workers 迁移。

Agent

▸ exec_command {"cmd":"$env:npm_config_registry='https://registry.npmmirror.com'; npx vinext...

Input

$ {"cmd":"$env:npm_config_registry='https://registry.npmmirror.com'; npx vinext@1.0.0-beta.10 init --platform=cloudflare --cdn-cache=none --image-optimization=none --prerender","justification":"","login...

Agent

▸ write_stdin {"chars":"y\n","max_output_tokens":24000,"session_id":24265,"yield_time_ms":3...

Input

{
  "chars": "y\n",
  "max_output_tokens": 24000,
  "session_id": 24265,
  "yield_time_ms": 30000
}

Agent

vinext 自动安装选了不匹配的最新 react-server-dom-webpack@19.3.0，而 Next.js 当前锁定 React 19.2.8。我要改为 React 19.2.8 对应的匹配版本，避免用 --force 掩盖真实冲突。

Agent

▸ exec_command {"cmd":"Get-Content -Raw package.json; Get-ChildItem -Force | Select-Object N...

Input

$ {"cmd":"Get-Content -Raw package.json; Get-ChildItem -Force | Select-Object Name,Length,LastWriteTime","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":...

Agent

▸ exec_command {"cmd":"npm view react-server-dom-webpack@19.2.8 version peerDependencies --j...

Input

$ {"cmd":"npm view react-server-dom-webpack@19.2.8 version peerDependencies --json --registry=https://registry.npmmirror.com; npm view @vinext/cloudflare@1.0.0-beta.8 peerDependencies --json --registry=...

Agent

▸ exec_command {"cmd":"Get-Content -Raw vite.config.ts; Get-Content -Raw wrangler.jsonc","ju...

Input

$ {"cmd":"Get-Content -Raw vite.config.ts; Get-Content -Raw wrangler.jsonc","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powers...

Agent

▸ exec_command {"cmd":"npm view vinext@1.0.0-beta.10 dependencies peerDependencies optionalD...

Input

$ {"cmd":"npm view vinext@1.0.0-beta.10 dependencies peerDependencies optionalDependencies --json --registry=https://registry.npmmirror.com","justification":"","login":true,"max_output_tokens":12000,"pr...

Agent

vinext init 已生成 Cloudflare 配置，但依赖安装因 React 19.2 与最新 RSDW 19.3 冲突而中止。我会安装与 Next.js 当前 React 版本一致的依赖，并补上 Cloudflare Vite 插件和 Wrangler。

Agent

▸ exec_command {"cmd":"npm view @cloudflare/vite-plugin version --registry=https://registry....

Input

$ {"cmd":"npm view @cloudflare/vite-plugin version --registry=https://registry.npmmirror.com; npm view vite version --registry=https://registry.npmmirror.com; npm view @vitejs/plugin-react version --reg...

Agent

▸ exec_command {"cmd":"npm view react-server-dom-webpack@19.2.8 dist.tarball --registry=http...

Input

$ {"cmd":"npm view react-server-dom-webpack@19.2.8 dist.tarball --registry=https://registry.npmmirror.com; npm view @vinext/cloudflare@1.0.0-beta.8 dist.tarball --registry=https://registry.npmmirror.com...

Agent

▸ exec_command {"cmd":"npm install -D vinext@1.0.0-beta.10 @vinext/cloudflare@1.0.0-beta.8 v...

Input

$ {"cmd":"npm install -D vinext@1.0.0-beta.10 @vinext/cloudflare@1.0.0-beta.8 vite@8.3.0 @vitejs/plugin-react@6.1.1 @vitejs/plugin-rsc@0.5.35 @cloudflare/vite-plugin@1.56.0 react-server-dom-webpack@19.2...

Agent

▸ exec_command {"cmd":"npx vinext check","justification":"","login":true,"max_output_tokens"...

Input

$ {"cmd":"npx vinext check","justification":"","login":true,"max_output_tokens":15000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\...

Agent

▸ exec_command {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tok...

Input

$ {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tokens":24000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderpro...

Agent

▸ exec_command {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens"...

Input

$ {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens":16000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\...

Agent

兼容性 100%，vinext 生产构建成功。现在检查 Cloudflare CLI 登录状态和生成的 Worker 配置，然后部署。

Agent

▸ exec_command {"cmd":"npx wrangler whoami","justification":"","login":true,"max_output_toke...

Input

$ {"cmd":"npx wrangler whoami","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproo...

Agent

▸ exec_command {"cmd":"Get-Content -Raw dist\\server\\wrangler.json; Get-Content -Raw wrangl...

Input

$ {"cmd":"Get-Content -Raw dist\\server\\wrangler.json; Get-Content -Raw wrangler.jsonc; Get-Content -Raw package.json","justification":"","login":true,"max_output_tokens":18000,"prefix_rule":[],"sandbo...

Agent

Cloudflare CLI 本机登录已过期，需要在浏览器重新授权。我现在启动 Wrangler 登录。

Agent

▸ exec_command {"cmd":"npx wrangler login","justification":"","login":true,"max_output_token...

Input

$ {"cmd":"npx wrangler login","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":true,"workdir":"G:\\视频处理工具\\renderproof\...

Agent

Cloudflare 登录页已在浏览器打开。请在页面里授权 Wrangler 访问你的账号；完成后我会继续部署。

Agent

▸ write_stdin {"chars":"","max_output_tokens":8000,"session_id":70556,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 8000,
  "session_id": 70556,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"n\n","max_output_tokens":8000,"session_id":70556,"yield_time_ms":30...

Input

{
  "chars": "n\n",
  "max_output_tokens": 8000,
  "session_id": 70556,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"n\n","max_output_tokens":8000,"session_id":70556,"yield_time_ms":30...

Input

{
  "chars": "n\n",
  "max_output_tokens": 8000,
  "session_id": 70556,
  "yield_time_ms": 30000
}

Agent

Cloudflare 授权成功。部署前我会把非敏感配置写进 wrangler.jsonc，密钥只通过 wrangler secret put 安全写入，不放进文件或命令参数。

Agent

▸ exec_command {"cmd":"Get-Content -Raw wrangler.jsonc; Get-Content -Raw dist\\server\\wrang...

Input

$ {"cmd":"Get-Content -Raw wrangler.jsonc; Get-Content -Raw dist\\server\\wrangler.json","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","s...

Agent

▸ exec_command {"cmd":"Get-Content -Raw wrangler.jsonc; Get-Content -Raw dist\\server\\wrang...

Input

$ {"cmd":"Get-Content -Raw wrangler.jsonc; Get-Content -Raw dist\\server\\wrangler.json","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","s...

Agent

▸ exec_command {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tok...

Input

$ {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tokens":24000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderpro...

Agent

配置已带非敏感变量和可观测性，生产构建成功。现在部署 Worker 到你的 Cloudflare 账号。

Agent

▸ exec_command {"cmd":"npm run deploy:vinext","justification":"","login":true,"max_output_to...

Input

$ {"cmd":"npm run deploy:vinext","justification":"","login":true,"max_output_tokens":24000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":true,"workdir":"G:\\视频处理工具\\renderpro...

Agent

Worker 已成功部署到：

https://renderproof-web.ztxf5168c.workers.dev

现在把两个密钥安全写入 Cloudflare Secret，顺序逐个执行，不显示密钥内容。

Agent

▸ exec_command {"cmd":"$line = Get-Content '.env.local' | Where-Object { $_ -match '^SANITY_...

Input

$ {"cmd":"$line = Get-Content '.env.local' | Where-Object { $_ -match '^SANITY_API_READ_TOKEN=' } | Select-Object -First 1; if (-not $line) { throw 'SANITY_API_READ_TOKEN missing' }; $value = $line.Subs...

Agent

▸ exec_command {"cmd":"$line = Get-Content '.env.local' | Where-Object { $_ -match '^SANITY_...

Input

$ {"cmd":"$line = Get-Content '.env.local' | Where-Object { $_ -match '^SANITY_API_READ_TOKEN=' } | Select-Object -First 1; if (-not $line) { throw 'SANITY_API_READ_TOKEN missing' }; $value = $line.Subs...

Agent

▸ exec_command {"cmd":"$line = Get-Content '.env.local' | Where-Object { $_ -match '^DEEPSEE...

Input

$ {"cmd":"$line = Get-Content '.env.local' | Where-Object { $_ -match '^DEEPSEEK_API_KEY=' } | Select-Object -First 1; if (-not $line) { throw 'DEEPSEEK_API_KEY missing' }; $value = $line.Substring($lin...

Agent

两个 Cloudflare Secret 已写入。现在对线上 URL 执行同一套端到端烟雾测试，验证部署环境的变量、MCP 和模型调用。

Agent

▸ exec_command {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof-web.ztxf5168c.workers...

Input

$ {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof-web.ztxf5168c.workers.dev/api/chat'; npm run smoke","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissi...

Agent

▸ exec_command {"cmd":"curl.exe -s -o NUL -w \"STATUS=%{http_code}\\nCONTENT_TYPE=%{content_...

Input

$ {"cmd":"curl.exe -s -o NUL -w \"STATUS=%{http_code}\\nCONTENT_TYPE=%{content_type}\\n\" https://renderproof-web.ztxf5168c.workers.dev/","justification":"","login":true,"max_output_tokens":4000,"prefix...

Agent

刚才的测试命令被 shell 解释错了，实际只测了本地版本。现在改用 PowerShell 正确设置目标 URL，并给 Cloudflare 首次启动更长的超时时间。

Agent

▸ exec_command {"cmd":"curl.exe -L -sS --max-time 90 -o NUL -w \"STATUS=%{http_code}`nCONTEN...

Input

$ {"cmd":"curl.exe -L -sS --max-time 90 -o NUL -w \"STATUS=%{http_code}`nCONTENT_TYPE=%{content_type}`nTOTAL=%{time_total}`n\" https://renderproof-web.ztxf5168c.workers.dev/","justification":"","login":...

Agent

▸ exec_command {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof-web.ztxf5168c.workers...

Input

$ {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof-web.ztxf5168c.workers.dev/api/chat'; npm run smoke","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissi...

Agent

部署成功，但当前网络无法连接 workers.dev 域，这是中国大陆常见的访问限制。为了让评委和你都能打开，需要绑定你 Cloudflare 账号里的自定义域名。

Agent

▸ exec_command {"cmd":"Resolve-DnsName renderproof-web.ztxf5168c.workers.dev -ErrorAction Si...

Input

$ {"cmd":"Resolve-DnsName renderproof-web.ztxf5168c.workers.dev -ErrorAction SilentlyContinue | Select-Object Name,Type,IPAddress,NameHost; Test-NetConnection renderproof-web.ztxf5168c.workers.dev -Port...

Agent

Cloudflare Worker 已部署成功，但 workers.dev 域名在当前网络连接超时，因此还不能作为最终 Demo 地址。Cloudflare 访问密钥也已经安全写入。

请告诉我一个已经在你的 Cloudflare 账号中管理的域名，只需要域名本身，例如：

example.com

我会把 Demo 绑定为：

renderproof.example.com

不需要发送 Cloudflare 账号、密码或 API Token。拿到域名后，我会添加 Custom Domain、重新部署，并从现有网站域名测试线上接口。

使用子域名 renderproof.proteinpayment.com。我会把它作为 Worker 的 Custom Domain 写入配置，然后重新部署并测试线上接口。

Agent

▸ exec_command {"cmd":"npm run deploy:vinext","justification":"","login":true,"max_output_to...

Input

$ {"cmd":"npm run deploy:vinext","justification":"","login":true,"max_output_tokens":22000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":true,"workdir":"G:\\视频处理工具\\renderpro...

Agent

Custom Domain 已绑定并部署完成。现在测试 https://renderproof.proteinpayment.com 的页面和完整 AI/MCP 链路。

Agent

▸ exec_command {"cmd":"curl.exe -L -sS --max-time 90 -o NUL -w \"STATUS=%{http_code}`nCONTEN...

Input

$ {"cmd":"curl.exe -L -sS --max-time 90 -o NUL -w \"STATUS=%{http_code}`nCONTENT_TYPE=%{content_type}`nTOTAL=%{time_total}`n\" https://renderproof.proteinpayment.com/","justification":"","login":true,"m...

Agent

▸ exec_command {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof.proteinpayment.com/ap...

Input

$ {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof.proteinpayment.com/api/chat'; npm run smoke","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"u...

Agent

页面已能访问，但线上 API 返回 502。现在读取 Worker 实时日志定位是环境变量、MCP 还是 DeepSeek 连接问题。

Agent

▸ exec_command {"cmd":"npx wrangler tail renderproof-web --format pretty","justification":""...

Input

$ {"cmd":"npx wrangler tail renderproof-web --format pretty","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":true,"wor...

Agent

▸ exec_command {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof.proteinpayment.com/ap...

Input

$ {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof.proteinpayment.com/api/chat'; npm run smoke","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"u...

Agent

▸ exec_command {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof.proteinpayment.com/ap...

Input

$ {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof.proteinpayment.com/api/chat'; npm run smoke","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"u...

Agent

▸ write_stdin {"chars":"","max_output_tokens":16000,"session_id":75286,"yield_time_ms":5000}

Input

{
  "chars": "",
  "max_output_tokens": 16000,
  "session_id": 75286,
  "yield_time_ms": 5000
}

Agent

502 已定位为 Worker 出站 TCP 超时。需要确认超时目标是 DeepSeek 还是 Sanity API，再调整 Worker 的网络路径。

Agent

▸ exec_command {"cmd":"Resolve-DnsName api.deepseek.com -Type A | Select-Object Name,IPAddre...

Input

$ {"cmd":"Resolve-DnsName api.deepseek.com -Type A | Select-Object Name,IPAddress; Resolve-DnsName api.sanity.io -Type A | Select-Object Name,IPAddress","justification":"","login":true,"max_output_token...

Agent

▸ exec_command {"cmd":"curl.exe -sS --max-time 20 -o NUL -w \"SANITY_STATUS=%{http_code} IP=...

Input

$ {"cmd":"curl.exe -sS --max-time 20 -o NUL -w \"SANITY_STATUS=%{http_code} IP=%{remote_ip}\\n\" https://api.sanity.io/; curl.exe -sS --max-time 20 -o NUL -w \"DEEPSEEK_STATUS=%{http_code} IP=%{remote_i...

Agent

▸ exec_command {"cmd":"Resolve-DnsName api.deepseek.com -Type A | Select-Object Name,IPAddre...

Input

$ {"cmd":"Resolve-DnsName api.deepseek.com -Type A | Select-Object Name,IPAddress; Resolve-DnsName api.sanity.io -Type A | Select-Object Name,IPAddress","justification":"","login":true,"max_output_token...

Agent

▸ exec_command {"cmd":"Resolve-DnsName 185.45.6.103 -Type PTR -ErrorAction SilentlyContinue ...

Input

$ {"cmd":"Resolve-DnsName 185.45.6.103 -Type PTR -ErrorAction SilentlyContinue | Select-Object NameHost,Name,IPAddress","justification":"","login":true,"max_output_tokens":4000,"prefix_rule":[],"sandbox...

Agent

▸ exec_command {"cmd":"Invoke-RestMethod -Uri 'https://ipinfo.io/185.45.6.103/json' | Conver...

Input

$ {"cmd":"Invoke-RestMethod -Uri 'https://ipinfo.io/185.45.6.103/json' | ConvertTo-Json -Depth 4","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"use_def...

Agent

▸ exec_command {"cmd":"Invoke-RestMethod -Uri 'https://ipinfo.io/185.45.6.103/json' | Conver...

Input

$ {"cmd":"Invoke-RestMethod -Uri 'https://ipinfo.io/185.45.6.103/json' | ConvertTo-Json -Depth 4","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"use_def...

Agent

▸ exec_command {"cmd":"npx wrangler tail renderproof-web --format json","justification":"","...

Input

$ {"cmd":"npx wrangler tail renderproof-web --format json","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":true,"workd...

Agent

▸ write_stdin {"chars":"","max_output_tokens":5000,"session_id":2951,"yield_time_ms":3000}

Input

{
  "chars": "",
  "max_output_tokens": 5000,
  "session_id": 2951,
  "yield_time_ms": 3000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":5000,"session_id":2951,"yield_time_ms":5000}

Input

{
  "chars": "",
  "max_output_tokens": 5000,
  "session_id": 2951,
  "yield_time_ms": 5000
}

Agent

wrangler tail 本身被当前网络劫持到不可达 IP，拿不到云端日志。我会给 API 增加不泄露秘密的阶段码（mcp-connect、mcp-tools、model-start），重新部署后即可精确定位 502。

Agent

▸ exec_command {"cmd":"npm run deploy:vinext","justification":"","login":true,"max_output_to...

Input

$ {"cmd":"npm run deploy:vinext","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":true,"workdir":"G:\\视频处理工具\\renderpro...

Agent

▸ exec_command {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'What does crf_ma...

Input

$ {"cmd":"$body = @{ messages = @(@{ role = 'user'; content = 'What does crf_max do?' }) } | ConvertTo-Json -Depth 5; try { $r = Invoke-WebRequest -Method Post -Uri 'https://renderproof.proteinpayment.c...

Agent

▸ exec_command {"cmd":"$body = '{\"messages\":[{\"role\":\"user\",\"content\":\"What does cr...

Input

$ {"cmd":"$body = '{\"messages\":[{\"role\":\"user\",\"content\":\"What does crf_max do?\"}]}'; curl.exe -sS --max-time 120 -w \"`nSTATUS=%{http_code}`n\" -H \"Content-Type: application/json\" --data-bi...

Agent

阶段码确认失败发生在 createMCPClient。我加入一个临时诊断路由，用原生 fetch 分别测试 Sanity 基础 API 和 MCP 的 tools/list，定位是网络、鉴权还是 transport 协议问题。

Agent

▸ exec_command {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tok...

Input

$ {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproo...

Agent

▸ exec_command {"cmd":"npx wrangler deploy --config dist/server/wrangler.json","justificatio...

Input

$ {"cmd":"npx wrangler deploy --config dist/server/wrangler.json","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false...

Agent

▸ exec_command {"cmd":"curl.exe -sS --max-time 60 https://renderproof.proteinpayment.com/api...

Input

$ {"cmd":"curl.exe -sS --max-time 60 https://renderproof.proteinpayment.com/api/diagnostics/mcp","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":"use_defa...

Agent

原生 MCP tools/list 在 Worker 里成功，说明网络和 Token 都没问题；失败来自 AI SDK MCP 客户端本身。我把诊断路由扩展到直接实例化该客户端并返回错误信息。

Agent

▸ exec_command {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tok...

Input

$ {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproo...

Agent

▸ exec_command {"cmd":"npx wrangler deploy --config dist/server/wrangler.json","justificatio...

Input

$ {"cmd":"npx wrangler deploy --config dist/server/wrangler.json","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false...

Agent

▸ exec_command {"cmd":"curl.exe -sS --max-time 60 https://renderproof.proteinpayment.com/api...

Input

$ {"cmd":"curl.exe -sS --max-time 60 https://renderproof.proteinpayment.com/api/diagnostics/mcp","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_def...

Agent

▸ exec_command {"cmd":"$p='node_modules\\@ai-sdk\\mcp\\dist\\index.d.ts'; $lines=Get-Content...

Input

$ {"cmd":"$p='node_modules\\@ai-sdk\\mcp\\dist\\index.d.ts'; $lines=Get-Content $p; $lines[780..865]","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"us...

Agent

▸ exec_command {"cmd":"rg -n \"type MCPTransportConfig|interface.*HTTP|redirect\" node_modul...

Input

$ {"cmd":"rg -n \"type MCPTransportConfig|interface.*HTTP|redirect\" node_modules/@ai-sdk/mcp/dist/index.d.ts | Select-Object -First 80","justification":"","login":true,"max_output_tokens":10000,"prefix...

Agent

▸ exec_command {"cmd":"rg -n \"type MCPTransportConfig|redirect\" node_modules/@ai-sdk/mcp/d...

Input

$ {"cmd":"rg -n \"type MCPTransportConfig|redirect\" node_modules/@ai-sdk/mcp/dist/index.d.ts | Select-Object -First 80","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandb...

Agent

根因明确：@ai-sdk/mcp 默认把 HTTP redirect 设为 error，Cloudflare Workers 不支持该值，只支持 follow 或 manual。在 transport 配置里显式设置 redirect: "follow" 即可。

Agent

▸ exec_command {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tok...

Input

$ {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproo...

Agent

▸ exec_command {"cmd":"npx wrangler deploy --config dist/server/wrangler.json","justificatio...

Input

$ {"cmd":"npx wrangler deploy --config dist/server/wrangler.json","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false...

Agent

▸ exec_command {"cmd":"curl.exe -sS --max-time 60 https://renderproof.proteinpayment.com/api...

Input

$ {"cmd":"curl.exe -sS --max-time 60 https://renderproof.proteinpayment.com/api/diagnostics/mcp","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_def...

Agent

MCP SDK 在 Cloudflare 中已修复并成功列出工具。现在先跑线上完整烟雾测试；通过后我会删除临时诊断路由。

Agent

▸ exec_command {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof.proteinpayment.com/ap...

Input

$ {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof.proteinpayment.com/api/chat'; npm run smoke","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"u...

Agent

▸ exec_command {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tok...

Input

$ {"cmd":"npm run build:vinext","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproo...

Agent

▸ exec_command {"cmd":"npx wrangler deploy --config dist/server/wrangler.json","justificatio...

Input

$ {"cmd":"npx wrangler deploy --config dist/server/wrangler.json","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false...

Agent

▸ exec_command {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof.proteinpayment.com/ap...

Input

$ {"cmd":"$env:RENDERPROOF_SMOKE_URL='https://renderproof.proteinpayment.com/api/chat'; npm run smoke","justification":"","login":true,"max_output_tokens":20000,"prefix_rule":[],"sandbox_permissions":"u...

Agent

线上接口已通过全部测试。现在用浏览器打开生产页面，确认 UI、静态资源和响应式展示正常。

Agent

▸ js {"code":"await tab.goto('https://renderproof.proteinpayment.com'); await tab....

Input

{
  "code": "await tab.goto('https://renderproof.proteinpayment.com'); await tab.getAXStateAndScreenshot();",
  "title": "Verify production UI"
}

Agent

▸ js {"code":"let productionTab = await cua.createBrowserTab(\"iab\", \"https://re...

Input

{
  "code": "let productionTab = await cua.createBrowserTab(\"iab\", \"https://renderproof.proteinpayment.com\", { visible: true });",
  "title": "Open production RenderProof"
}

Agent

生产接口已通过端到端测试，路径为 https://renderproof.proteinpayment.com/api/chat。我现在整理并提交 Cloudflare 部署改动，然后准备代码仓库和 DEV 提交材料。

Agent

▸ exec_command {"cmd":"git status --short; git diff --stat; git remote -v","justification":"...

Input

$ {"cmd":"git status --short; git diff --stat; git remote -v","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"w...

Agent

▸ exec_command {"cmd":"gh --version; gh auth status","justification":"","login":true,"max_ou...

Input

$ {"cmd":"gh --version; gh auth status","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\r...

Agent

▸ exec_command {"cmd":"git check-ignore -v web/.env.local web/.env.example web/dist/index.js...

Input

$ {"cmd":"git check-ignore -v web/.env.local web/.env.example web/dist/index.js studio/.sanity/runtime/app.js","justification":"","login":true,"max_output_tokens":6000,"prefix_rule":[],"sandbox_permissi...

Agent

▸ exec_command {"cmd":"npm run lint; npx tsc --noEmit","justification":"","login":true,"max_...

Input

$ {"cmd":"npm run lint; npx tsc --noEmit","justification":"","login":true,"max_output_tokens":14000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\...

Agent

▸ exec_command {"cmd":"npm run typecheck","justification":"","login":true,"max_output_tokens...

Input

$ {"cmd":"npm run typecheck","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\...

Agent

vinext 生成目录进入了 ESLint 扫描范围，且旧 .next 类型文件过期。我要让 lint 忽略构建输出，并重新生成 Next 类型后进行最终代码检查。

Agent

▸ exec_command {"cmd":"npm run build","justification":"","login":true,"max_output_tokens":16...

Input

$ {"cmd":"npm run build","justification":"","login":true,"max_output_tokens":16000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\web...

Agent

▸ exec_command {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":120...

Input

$ {"cmd":"npm run lint","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\web"...

Agent

▸ exec_command {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens"...

Input

$ {"cmd":"npx tsc --noEmit","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工具\\renderproof\\...

Agent

▸ exec_command {"cmd":"Get-Content -Raw .gitignore; git status --short","justification":"","...

Input

$ {"cmd":"Get-Content -Raw .gitignore; git status --short","justification":"","login":true,"max_output_tokens":10000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"work...

Agent

▸ exec_command {"cmd":"git status --short; git diff --stat","justification":"","login":true,...

Input

$ {"cmd":"git status --short; git diff --stat","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频...

Agent

▸ exec_command {"cmd":"Get-Content -Raw .gitignore","justification":"","login":true,"max_out...

Input

$ {"cmd":"Get-Content -Raw .gitignore","justification":"","login":true,"max_output_tokens":6000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":false,"workdir":"G:\\视频处理工...

Agent

▸ exec_command {"cmd":"git add .; git commit -m \"Deploy RenderProof to Cloudflare Workers\"...

Input

$ {"cmd":"git add .; git commit -m \"Deploy RenderProof to Cloudflare Workers\"","justification":"","login":true,"max_output_tokens":16000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"b...

Agent

部署和安全问题已经解决。下一步准备实际可发布的 DEV Path One 英文草稿，内容会基于当前真实实现，包括 29 条结构化文档、15 个 Knowledge Base entries、冲突处理与线上 Demo。

线上部署与端到端测试已经完成。我会停掉本地开发服务器，接下来只差代码仓库、Agent Session 和最终 DEV 发布。

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":5000,"session_id":10496,"yield_time_ms"...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 5000,
  "session_id": 10496,
  "yield_time_ms": 1000
}

Agent

Cloudflare 部署已经完全跑通：

公开 Demo：https://renderproof.proteinpayment.com
自定义域名：renderproof.proteinpayment.com
Cloudflare Worker：renderproof-web
Sanity Token 和 DeepSeek Key 已保存为 Worker Secrets
线上烟雾测试全部通过
MCP 正常调用 initial_context 和 knowledge_base_read
回答能拒绝未经证实的 MPEG-TS 备用容器
vinext 兼容性检查为 100%
Next.js 构建、Cloudflare 构建、Lint 和 TypeScript 检查均通过

DEV Path One 英文提交草稿也已准备好，位于 DEV-SUBMISSION.md。

下一步需要创建代码仓库。请打开 GitHub New Repository，创建：

Repository name：renderproof
可见性：Public
不要勾选 README、.gitignore 或 License

创建后把仓库 URL 发我，我会推送代码、补全提交草稿，并继续准备 Agent Session。

配置正确，点击 Create repository。

创建完成后仓库地址应是：

https://github.com/TZ005/renderproof

点击后回复“好了”，我会添加 Git remote 并推送全部代码。

我先配置 GitHub remote，然后推送当前 main 分支。

Agent

▸ exec_command {"cmd":"git remote -v; git status --short; git branch --show-current","justif...

Input

$ {"cmd":"git remote -v; git status --short; git branch --show-current","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty"...

Agent

▸ exec_command {"cmd":"git add docs/DEV-SUBMISSION.md; git commit -m \"Add DEV submission dr...

Input

$ {"cmd":"git add docs/DEV-SUBMISSION.md; git commit -m \"Add DEV submission draft\"; git remote add origin https://github.com/TZ005/renderproof.git; git remote -v; git push -u origin main","justificati...

Agent

GitHub 没有保存的登录凭据。我先取消这次命令行密码询问，改用 Windows Credential Manager 触发浏览器授权，避免在终端或聊天里输入密码。

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":3000,"session_id":77683,"yield_time_ms"...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 3000,
  "session_id": 77683,
  "yield_time_ms": 1000
}

Agent

▸ exec_command {"cmd":"git config --global --get credential.helper; git credential-manager -...

Input

$ {"cmd":"git config --global --get credential.helper; git credential-manager --version; Get-Command git-credential-manager -ErrorAction SilentlyContinue | Select-Object Source","justification":"","logi...

Agent

▸ exec_command {"cmd":"where.exe git-credential-manager; where.exe git-credential-manager-co...

Input

$ {"cmd":"where.exe git-credential-manager; where.exe git-credential-manager-core; where.exe gh","justification":"","login":true,"max_output_tokens":5000,"prefix_rule":[],"sandbox_permissions":"use_defa...

Agent

当前机器没有 GitHub CLI 或凭据管理器。为了使用浏览器登录并避免手工输入密码，我会安装官方 GitHub CLI，然后在浏览器中完成授权。

Agent

▸ exec_command {"cmd":"winget --version","justification":"","login":true,"max_output_tokens"...

Input

$ {"cmd":"winget --version","justification":"","login":true,"max_output_tokens":4000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"powershell","tty":false,"workdir":"G:\\视频处理工具\\renderpr...

Agent

系统没有 winget，我改用 GitHub 官方发布页的便携版 CLI，不改系统配置；授权仍在浏览器完成。

Agent

▸ exec_command {"cmd":"$release = Invoke-RestMethod -Uri 'https://api.github.com/repos/cli/c...

Input

$ {"cmd":"$release = Invoke-RestMethod -Uri 'https://api.github.com/repos/cli/cli/releases/latest' -Headers @{ 'User-Agent'='RenderProof-Setup' }; \"TAG=$($release.tag_name)\"; $asset = $release.assets ...

Agent

▸ exec_command {"cmd":"$tools='G:\\视频处理工具\\renderproof\\.tools'; New-Item -ItemType Director...

Input

$ {"cmd":"$tools='G:\\视频处理工具\\renderproof\\.tools'; New-Item -ItemType Directory -Force -Path $tools | Out-Null; $zip=Join-Path $tools 'gh.zip'; curl.exe -L --fail --retry 3 -o $zip 'https://github.com/...

Agent

▸ write_stdin {"chars":"","max_output_tokens":10000,"session_id":90810,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 10000,
  "session_id": 90810,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":10000,"session_id":90810,"yield_time_ms":60000}

Input

{
  "chars": "",
  "max_output_tokens": 10000,
  "session_id": 90810,
  "yield_time_ms": 60000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":10000,"session_id":90810,"yield_time_ms":60000}

Input

{
  "chars": "",
  "max_output_tokens": 10000,
  "session_id": 90810,
  "yield_time_ms": 60000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":10000,"session_id":90810,"yield_time_ms":60000}

Input

{
  "chars": "",
  "max_output_tokens": 10000,
  "session_id": 90810,
  "yield_time_ms": 60000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":10000,"session_id":90810,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 10000,
  "session_id": 90810,
  "yield_time_ms": 30000
}

Agent

GitHub CLI 便携版已下载并通过 SHA256 校验。我会把它排除在 Git 外，然后启动浏览器授权。

Agent

▸ exec_command {"cmd":".\\.tools\\bin\\gh.exe auth login --hostname github.com --git-protoco...

Input

$ {"cmd":".\\.tools\\bin\\gh.exe auth login --hostname github.com --git-protocol https --web","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_defaul...

Agent

▸ exec_command {"cmd":".\\.tools\\bin\\gh.exe auth login --hostname github.com --git-protoco...

Input

$ {"cmd":".\\.tools\\bin\\gh.exe auth login --hostname github.com --git-protocol https --web","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permissions":"use_defaul...

Agent

▸ write_stdin {"chars":"y\n","max_output_tokens":12000,"session_id":37229,"yield_time_ms":1...

Input

{
  "chars": "y\n",
  "max_output_tokens": 12000,
  "session_id": 37229,
  "yield_time_ms": 1000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":12000,"session_id":37229,"yield_time_ms":1000}

Input

{
  "chars": "",
  "max_output_tokens": 12000,
  "session_id": 37229,
  "yield_time_ms": 1000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":12000,"session_id":37229,"yield_time_ms":5000}

Input

{
  "chars": "",
  "max_output_tokens": 12000,
  "session_id": 37229,
  "yield_time_ms": 5000
}

Agent

GitHub CLI 的 web 登录接口在当前命令行网络超时，但浏览器访问正常。我先检查系统是否已有可用于 Git 的代理配置。

Agent

▸ exec_command {"cmd":"Get-ChildItem Env: | Where-Object { $_.Name -match 'proxy|http_proxy|...

Input

$ {"cmd":"Get-ChildItem Env: | Where-Object { $_.Name -match 'proxy|http_proxy|https_proxy' } | Select-Object Name,Value; git config --global --get http.proxy; git config --global --get https.proxy","ju...

Agent

▸ exec_command {"cmd":"Get-ChildItem Env: | Where-Object { $_.Name -match 'proxy|http_proxy|...

Input

$ {"cmd":"Get-ChildItem Env: | Where-Object { $_.Name -match 'proxy|http_proxy|https_proxy' } | Select-Object Name,Value; git config --global --get http.proxy; git config --global --get https.proxy","ju...

Agent

▸ exec_command {"cmd":"Resolve-DnsName ssh.github.com -Type A | Select-Object Name,IPAddress...

Input

$ {"cmd":"Resolve-DnsName ssh.github.com -Type A | Select-Object Name,IPAddress; Test-NetConnection ssh.github.com -Port 443 -InformationLevel Detailed","justification":"","login":true,"max_output_token...

Agent

▸ exec_command {"cmd":"Resolve-DnsName github.com -Type A | Select-Object Name,IPAddress; Te...

Input

$ {"cmd":"Resolve-DnsName github.com -Type A | Select-Object Name,IPAddress; Test-NetConnection github.com -Port 443 -InformationLevel Detailed","justification":"","login":true,"max_output_tokens":8000,...

Agent

▸ exec_command {"cmd":"Resolve-DnsName ssh.github.com -Type A | Select-Object Name,IPAddress...

Input

$ {"cmd":"Resolve-DnsName ssh.github.com -Type A | Select-Object Name,IPAddress; Test-NetConnection ssh.github.com -Port 443 -InformationLevel Detailed","justification":"","login":true,"max_output_token...

Agent

▸ exec_command {"cmd":"Resolve-DnsName github.com -Type A | Select-Object Name,IPAddress; Te...

Input

$ {"cmd":"Resolve-DnsName github.com -Type A | Select-Object Name,IPAddress; Test-NetConnection github.com -Port 443 -InformationLevel Detailed","justification":"","login":true,"max_output_tokens":8000,...

Agent

▸ exec_command {"cmd":"Test-NetConnection ssh.github.com -Port 443 | Format-List ComputerNam...

Input

$ {"cmd":"Test-NetConnection ssh.github.com -Port 443 | Format-List ComputerName,RemoteAddress,RemotePort,TcpTestSucceeded","justification":"","login":true,"max_output_tokens":4000,"prefix_rule":[],"san...

Agent

▸ exec_command {"cmd":"Test-NetConnection github.com -Port 443 | Format-List ComputerName,Re...

Input

$ {"cmd":"Test-NetConnection github.com -Port 443 | Format-List ComputerName,RemoteAddress,RemotePort,TcpTestSucceeded","justification":"","login":true,"max_output_tokens":4000,"prefix_rule":[],"sandbox...

Agent

GitHub 的 SSH 端口 443 可以连接，可以绕过超时的网页登录。我会生成专用 SSH Key，你只需把公钥添加到 GitHub；私钥留在本机，不发到聊天。

Agent

▸ exec_command {"cmd":"$key='C:\\Users\\Administrator\\.ssh\\renderproof_github'; if (-not (...

Input

$ {"cmd":"$key='C:\\Users\\Administrator\\.ssh\\renderproof_github'; if (-not (Test-Path $key)) { ssh-keygen -t ed25519 -C 'renderproof@TZ005' -f $key -N '\"\"' }; Get-Content ($key + '.pub')","justific...

Agent

GitHub SSH 端口 443 可用。现在把专用公钥添加到你的 GitHub 账号：

打开 GitHub SSH Keys
Title 填写：RenderProof Cloudflare Deploy
Key type 选择：Authentication Key
Key 粘贴下面这一整行：

ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIEYgir69IbU2DtnqUWHXIfaBiamFZTtIfMdPtfMz++EY renderproof@TZ005

点击 Add SSH key

私钥保留在本机 [REDACTED]\.ssh\renderproof_github，不会发到聊天。添加完成后回复“好了”，我会通过 SSH 推送代码。

我先验证 GitHub SSH 身份，然后切换 remote 并推送。

Agent

▸ exec_command {"cmd":"ssh -T -p 443 -i 'C:\\Users\\Administrator\\.ssh\\renderproof_github'...

Input

$ {"cmd":"ssh -T -p 443 -i 'C:\\Users\\Administrator\\.ssh\\renderproof_github' -o IdentitiesOnly=yes -o StrictHostKeyChecking=accept-new git@ssh.github.com","justification":"","login":true,"max_output_...

Agent

▸ write_stdin {"chars":"","max_output_tokens":6000,"session_id":83837,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 6000,
  "session_id": 83837,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":6000,"session_id":83837,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 6000,
  "session_id": 83837,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"","max_output_tokens":6000,"session_id":83837,"yield_time_ms":30000}

Input

{
  "chars": "",
  "max_output_tokens": 6000,
  "session_id": 83837,
  "yield_time_ms": 30000
}

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":6000,"session_id":83837,"yield_time_ms"...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 6000,
  "session_id": 83837,
  "yield_time_ms": 1000
}

Agent

SSH 握手后无响应，我先用详细日志定位是端口、主机还是密钥认证阶段的问题。

Agent

▸ exec_command {"cmd":"ssh -vv -T -p 443 -i 'C:\\Users\\Administrator\\.ssh\\renderproof_git...

Input

$ {"cmd":"ssh -vv -T -p 443 -i 'C:\\Users\\Administrator\\.ssh\\renderproof_github' -o IdentitiesOnly=yes -o ConnectTimeout=15 -o ServerAliveInterval=5 -o ServerAliveCountMax=3 git@ssh.github.com","just...

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":3000,"session_id":7326,"yield_time_ms":...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 3000,
  "session_id": 7326,
  "yield_time_ms": 1000
}

Agent

▸ exec_command {"cmd":"ssh -vv -T -p 443 -i 'C:\\Users\\Administrator\\.ssh\\renderproof_git...

Input

$ {"cmd":"ssh -vv -T -p 443 -i 'C:\\Users\\Administrator\\.ssh\\renderproof_github' -o IdentitiesOnly=yes -o IdentityAgent=none -o AddKeysToAgent=no -o ConnectTimeout=15 -o ServerAliveInterval=5 -o Serv...

Agent

▸ write_stdin {"chars":"\u0003","max_output_tokens":2000,"session_id":46829,"yield_time_ms"...

Input

{
  "chars": "\u0003",
  "max_output_tokens": 2000,
  "session_id": 46829,
  "yield_time_ms": 1000
}

Agent

▸ exec_command {"cmd":"ssh-keygen -y -P '' -f 'C:\\Users\\Administrator\\.ssh\\renderproof_g...

Input

$ {"cmd":"ssh-keygen -y -P '' -f 'C:\\Users\\Administrator\\.ssh\\renderproof_github'","justification":"","login":true,"max_output_tokens":4000,"prefix_rule":[],"sandbox_permissions":"use_default","shel...

Agent

发现问题：密钥因为参数引号错误实际带了 "" 口令，导致 SSH 卡在签名阶段。我会重新生成无口令的专用密钥，并给你新的公钥替换 GitHub 上刚添加的那一条。

Agent

▸ exec_command {"cmd":"$key='C:\\Users\\Administrator\\.ssh\\renderproof_github'; if (Test-P...

Input

$ {"cmd":"$key='C:\\Users\\Administrator\\.ssh\\renderproof_github'; if (Test-Path -LiteralPath $key) { Remove-Item -LiteralPath $key -Force }; if (Test-Path -LiteralPath ($key + '.pub')) { Remove-Item ...

Agent

▸ exec_command {"cmd":"$key='C:\\Users\\Administrator\\.ssh\\renderproof_github2'; ssh-keyge...

Input

$ {"cmd":"$key='C:\\Users\\Administrator\\.ssh\\renderproof_github2'; ssh-keygen -t ed25519 -C 'renderproof@TZ005' -f $key -N '' -q; ssh-keygen -y -P '' -f $key | Out-Null; if ($LASTEXITCODE -ne 0) { th...

Agent

新密钥已经生成并验证无口令。请在 GitHub 再添加这一条公钥：

打开 GitHub SSH Keys

Title：RenderProof Deploy 2
Key type：Authentication Key
Key：

ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIKk3DaVWYSDQvVD8JMJD/xznk1zCk9Xi6olgwsDOb2ON renderproof@TZ005

点击 Add SSH key 后回复“好了”。私钥仍只保存在本机。

Agent

▸ exec_command {"cmd":"ssh -T -p 443 -i 'C:\\Users\\Administrator\\.ssh\\renderproof_github2...

Input

$ {"cmd":"ssh -T -p 443 -i 'C:\\Users\\Administrator\\.ssh\\renderproof_github2' -o IdentitiesOnly=yes -o IdentityAgent=none -o ConnectTimeout=15 -o ServerAliveInterval=5 -o ServerAliveCountMax=3 git@ss...

Agent

SSH 认证成功，身份确认为 TZ005。现在切换 Git remote 到 SSH 端口 443，并提交最后的忽略规则后推送。

Agent

▸ exec_command {"cmd":"git status --short; git remote -v","justification":"","login":true,"m...

Input

$ {"cmd":"git status --short; git remote -v","justification":"","login":true,"max_output_tokens":6000,"prefix_rule":[],"sandbox_permissions":"use_default","shell":"bash","tty":false,"workdir":"G:\\视频处理工...

Agent

▸ exec_command {"cmd":"git add .gitignore; git commit -m \"Ignore portable tooling\"; git re...

Input

$ {"cmd":"git add .gitignore; git commit -m \"Ignore portable tooling\"; git remote set-url origin ssh://git@ssh.github.com:443/TZ005/renderproof.git; git config core.sshCommand \"ssh -i C:/Users/Admini...

Agent

Git 使用的是内置 Bash，SSH Key 路径需要用 Git Bash 格式。我会修正路径并重新推送。

Agent

▸ write_stdin {"chars":"n\n","max_output_tokens":3000,"session_id":27938,"yield_time_ms":1000}

Input

{
  "chars": "n\n",
  "max_output_tokens": 3000,
  "session_id": 27938,
  "yield_time_ms": 1000
}

Agent

▸ write_stdin {"chars":"no\n","max_output_tokens":3000,"session_id":27938,"yield_time_ms":1...

Input

{
  "chars": "no\n",
  "max_output_tokens": 3000,
  "session_id": 27938,
  "yield_time_ms": 1000
}

Agent

▸ exec_command {"cmd":"git config core.sshCommand \"ssh -i /c/Users/Administrator/.ssh/rende...

Input

$ {"cmd":"git config core.sshCommand \"ssh -i /c/Users/Administrator/.ssh/renderproof_github2 -o IdentitiesOnly=yes -o IdentityAgent=none -o StrictHostKeyChecking=accept-new\"; git push -u origin main",...

Agent

▸ exec_command {"cmd":"bash -lc 'pwd; ls -la /c/Users/Administrator/.ssh; ls -la /mnt/c/User...

Input

$ {"cmd":"bash -lc 'pwd; ls -la /c/Users/Administrator/.ssh; ls -la /mnt/c/Users/Administrator/.ssh'","justification":"","login":true,"max_output_tokens":8000,"prefix_rule":[],"sandbox_permissions":"use...

Agent

▸ exec_command {"cmd":"git config core.sshCommand \"ssh -i /mnt/c/Users/Administrator/.ssh/r...

Input

$ {"cmd":"git config core.sshCommand \"ssh -i /mnt/c/Users/Administrator/.ssh/renderproof_github2 -o IdentitiesOnly=yes -o IdentityAgent=none -o StrictHostKeyChecking=accept-new\"; git push -u origin ma...

Agent

Git 内置的 SSH 环境不读取 Windows ACL，私钥被视为权限过宽。我会把专用私钥复制到 Git Bash 自己的 ~/.ssh 并设为 600，仍不离开本机。

Agent

▸ exec_command {"cmd":"bash -lc 'mkdir -p ~/.ssh && cp /mnt/c/Users/Administrator/.ssh/rende...

Input

$ {"cmd":"bash -lc 'mkdir -p ~/.ssh && cp /mnt/c/Users/Administrator/.ssh/renderproof_github2 ~/.ssh/renderproof_github2 && chmod 600 ~/.ssh/renderproof_github2 && ssh -T -p 443 -i ~/.ssh/renderproof_gi...

Agent

▸ exec_command {"cmd":"git config core.sshCommand \"ssh -i /root/.ssh/renderproof_github2 -o...

Input

$ {"cmd":"git config core.sshCommand \"ssh -i /root/.ssh/renderproof_github2 -o IdentitiesOnly=yes -o IdentityAgent=none -o StrictHostKeyChecking=accept-new\"; git push -u origin main","justification":"...

Agent

代码已经推送到 GitHub。现在补全 DEV 提交草稿中的代码链接并推送最终文档更新。

Agent

▸ exec_command {"cmd":"git add docs/DEV-SUBMISSION.md; git commit -m \"Add repository link t...

Input

$ {"cmd":"git add docs/DEV-SUBMISSION.md; git commit -m \"Add repository link to submission draft\"; git push","justification":"","login":true,"max_output_tokens":12000,"prefix_rule":[],"sandbox_permiss...

Agent

代码仓库已完成并公开。最后两个提交材料是 Agent Session 和 DEV 发布。我先读取 Dev Agent Sessions 的上传格式与限制，再准备可直接上传的文件。

Agent

▸ exec_command {"cmd":"$r=Invoke-WebRequest -Uri 'https://dev.to/agent_sessions/new' -UseBas...

Input

$ {"cmd":"$r=Invoke-WebRequest -Uri 'https://dev.to/agent_sessions/new' -UseBasicParsing; \"STATUS=$($r.StatusCode)\"; $t=$r.Content -replace '<script[\\s\\S]*?</script>',' ' -replace '<style[\\s\\S]*?<...

Agent

▸ exec_command {"cmd":"$r=Invoke-WebRequest -Uri 'https://dev.to/devteam/share-embed-and-cur...

Input

$ {"cmd":"$r=Invoke-WebRequest -Uri 'https://dev.to/devteam/share-embed-and-curate-agent-sessions-on-dev-beta-5bj6' -UseBasicParsing; \"STATUS=$($r.StatusCode)\"; $t=$r.Content -replace '<script[\\s\\S]...

Agent

▸ exec_command {"cmd":"$r=Invoke-WebRequest -Uri 'https://dev.to/agent_sessions/new' -UseBas...

Input

$ {"cmd":"$r=Invoke-WebRequest -Uri 'https://dev.to/agent_sessions/new' -UseBasicParsing; \"STATUS=$($r.StatusCode)\"; $t=$r.Content -replace '<script[\\s\\S]*?</script>',' ' -replace '<style[\\s\\S]*?<...

Agent

▸ exec_command {"cmd":"$r=Invoke-WebRequest -Uri 'https://dev.to/devteam/share-embed-and-cur...

Input

$ {"cmd":"$r=Invoke-WebRequest -Uri 'https://dev.to/devteam/share-embed-and-curate-agent-sessions-on-dev-beta-5bj6' -UseBasicParsing; \"STATUS=$($r.StatusCode)\"; $t=$r.Content -replace '<script[\\s\\S]...

Agent

Agent Session 可以直接在 DEV 页面手动上传，不必索取 API Key。我先定位当前 Codex 会话的 .jsonl 文件并复制一份可上传版本。

Agent

▸ exec_command {"cmd":"Get-ChildItem -Path 'C:\\Users\\Administrator\\.codex\\sessions' -Rec...

Input

$ {"cmd":"Get-ChildItem -Path 'C:\\Users\\Administrator\\.codex\\sessions' -Recurse -Filter '*.jsonl' -ErrorAction SilentlyContinue | Sort-Object LastWriteTime -Descending | Select-Object -First 12 Full...

Agent

▸ exec_command {"cmd":"Get-ChildItem -Path 'C:\\Users\\Administrator\\.codex\\sessions' -Rec...

Input

$ {"cmd":"Get-ChildItem -Path 'C:\\Users\\Administrator\\.codex\\sessions' -Recurse -Filter '*.jsonl' -ErrorAction SilentlyContinue | Sort-Object LastWriteTime -Descending | Select-Object -First 12 Full...

Agent

▸ exec_command {"cmd":"Get-ChildItem -Path 'C:\\Users\\Administrator\\.codex\\sessions' -Rec...

Input

$ {"cmd":"Get-ChildItem -Path 'C:\\Users\\Administrator\\.codex\\sessions' -Recurse -Filter '*.jsonl' -ErrorAction SilentlyContinue | Sort-Object LastWriteTime -Descending | Select-Object -First 1 | For...

Agent

当前 Codex 会话约 31 MB，包含大量文件抓取与工具输出。我要先分析 JSONL 结构，然后生成一份已脱敏、保留关键开发过程的可上传版本。

Agent

▸ exec_command {"cmd":"$path='C:\\Users\\Administrator\\.codex\\sessions\\2026\\09\\19\\roll...

Input

$ {"cmd":"$path='C:\\Users\\Administrator\\.codex\\sessions\\2026\\09\\19\\rollout-2026-09-19T22-26-08-01a0ba0f-6f35-7602-8871-5ff1315caa59.jsonl'; $lines=Get-Content -LiteralPath $path -TotalCount 30; ...

Agent

▸ exec_command {"cmd":"$path='C:\\Users\\Administrator\\.codex\\sessions\\2026\\09\\19\\roll...

Input

$ {"cmd":"$path='C:\\Users\\Administrator\\.codex\\sessions\\2026\\09\\19\\rollout-2026-09-19T22-26-08-01a0ba0f-6f35-7602-8871-5ff1315caa59.jsonl'; $line=Get-Content -LiteralPath $path -TotalCount 20 | ...

Agent

▸ exec_command {"cmd":"$path='C:\\Users\\Administrator\\.codex\\sessions\\2026\\09\\19\\roll...

Input

$ {"cmd":"$path='C:\\Users\\Administrator\\.codex\\sessions\\2026\\09\\19\\rollout-2026-09-19T22-26-08-01a0ba0f-6f35-7602-8871-5ff1315caa59.jsonl'; $line=Get-Content -LiteralPath $path -TotalCount 1; $o...

Agent

DEV 支持 Codex .jsonl，但原文件 31 MB，且包含截图和早前暴露的密钥。我会生成一份只保留用户/助手对话与工具调用摘要的脱敏版本，供上传使用。

Agent

▸ exec_command {"cmd":"$source='C:\\Users\\Administrator\\.codex\\sessions\\2026\\09\\19\\ro...

Input

$ {"cmd":"$source='C:\\Users\\Administrator\\.codex\\sessions\\2026\\09\\19\\rollout-2026-09-19T22-26-08-01a0ba0f-6f35-7602-8871-5ff1315caa59.jsonl'; $outDir='G:\\视频处理工具\\renderproof\\.artifacts'; New-I...

What Makes This Agent Different

RenderProof is useful because the structured content changes the result:

It refuses to recommend nal-hrd=cbr for MP4 when the indexed source prohibits it.
It distinguishes "not documented" from "safe."
It preserves source provenance and conflict decisions.
It can generate FFmpeg commands only from options supported by retrieved evidence.
It reports missing destination or platform specifications instead of inventing limits.

The agent is not a generic video chatbot with a vector store attached. It is a constraint-oriented delivery assistant whose answers depend on the relationships and decisions stored in Sanity.

How I built a sourdough hydration calculator with Next.js 14 and Cloudflare Pages

Tim — Fri, 21 Aug 2026 14:25:54 +0000

Six months ago I got tired of doing hydration math on a napkin. So I built SourdoughCalc - a free calculator with 34 SEO-friendly articles.

Six months ago I got tired of doing hydration math on a napkin every time I baked sourdough at home. So I built SourdoughCalc - a free hydration calculator with 34 SEO-friendly articles. Here is what I learned shipping a zero-cost indie project.

The Stack

Next.js 14 App Router (TypeScript + Tailwind)
Cloudflare Pages for hosting ($0/mo, global CDN, sub-second TTFB)
Playwright for e2e tests
34 hand-written articles targeting sourdough keywords

Total monthly cost: $0.

The SEO Strategy That Worked

I did not try to rank for "sourdough" (impossible for a new site). Instead I targeted long-tail:

Keyword	Monthly Volume	Position
"65% hydration sourdough"	~7,200	Top 5
"sourdough hydration calculator"	~4,500	Top 3
"high vs low hydration sourdough"	~2,000	Top 10

Every article follows the same template:

Answer the question in the first 100 words
Add a FAQ schema block (FAQPage JSON-LD)
Link to 3-5 other articles in the cluster

What I Would Do Differently

GSC indexing speed: Do not batch-submit. Submit URL individually for faster pickup.
FAQ schema: Mandatory. Google shows FAQ rich snippets, which doubles CTR.
Comparison tables: I added 3-column comparison tables in 6 articles. They get featured snippets.

The Real Win

The calculator is the hook. 34 articles are the asset. SEO compounds. Six months in, I am getting 5x more organic traffic than the launch day spike ever was.

Try It

Free, no signup: sourdough-hydrationcalculator.com

If you have questions about the SEO or the build, drop a comment. I will answer.

How I tried five affiliate networks in one afternoon — and what the geo-friction taught me

Tim — Wed, 05 Aug 2026 14:00:23 +0000

I run a small affiliate site from China. Five programs in one Saturday afternoon: one working, one declined, one emailed, two dropped.

I run proteinpayment.com from China. It is a Hugo static-export site with 50+ articles about paying for whey, mass gainer, and protein boxes across India, Indonesia, Brazil, and Mexico. Cloudflare Pages hosts it, D1 backs the newsletter form, Resend sends the welcome email. The boring plumbing part of an Amazon affiliate site.

I spent Saturday afternoon applying to four more affiliate programs — LazyPay, Simpl, MyProtein, HealthKart — plus Impact.com, which I already had an account on. Here is what actually happened, all five in one day.

Amazon Associates: working since 2024

Nothing to report here. The boring fact is: this is the only program I have been paid through, and the only one that did not ask me where I live. Amazon Associates does not care if you are in Bangalore or Beijing. That alone made it the longest survivor in my stack.

Impact.com: marketplace declined, account still active

I applied to Impact.com to reach MyProtein and HealthKart via their catalog. Created the publisher account, accepted the Master Publisher Service Agreement, walked through every profile question. Two hours later I got a "marketplace application declined" notice.

The second line of that notice is the interesting one: "This does not affect any of your existing or pending brand program relationships. Your impact.com account can continue to be used when partnering directly with brands via their unique sign-up links and when accepting invitations to brand programs."

A polite way of saying: marketplace auto-reject is per-brand; the account itself is fine.

So I read the Master Publisher Service Agreement at app.impact.com. Zero hits for "China", "Chinese", "OFAC", or "sanctioned" anywhere in the agreement. The block is per-brand, not contractual. Section 3.1 mentions an Account Management Fees path applied "when Impact.com is unable to verify the information and/or documents submitted by Partner" — which matches a non-US bank / non-SSN KYC failure cleanly.

So Impact is not the destination. It is a tool for direct brand invites, which I have not yet triggered.

LazyPay: applied by email, awaiting response

LazyPay's footer shows "Partners" — but it is merchant partners, food and e-commerce brands. Clicking through gives a 403 on every URL variant I tried, including with a VPN node in Mumbai.

There is no public affiliate signup page. The path is email outreach only.

I sent to wecare@lazypay.in. Sent from my personal Gmail because the domain email yanxi002studio@proteinpayment.com is set up as receive-only via Cloudflare Email Routing — it has no SMTP, so I cannot send "as" it. Reply-To was not set; the lazy fallback was personal Gmail.

If LazyPay replies with a KYC path that needs an Indian phone, that will be the next place I find out whether India-resident KYC has any China alternative.

Simpl: dropped

The real "what" of this one: getsimpl.com is a Namecheap parking page. Not the company. Their main domain appears to be simpl.com (and / or getsimpl.in for the Indian variant), but none of those URLs led to an affiliate signup when I tried them either. Dropped from P2.

The lesson: when a domain returns a hosted-by-Namecheap placeholder, do not dig deeper. Walk away.

MyProtein: on hold

Was the original reason I signed up for Impact.com. The catalog refused my marketplace application, so the workaround path is direct outreach to affiliates@myprotein.com asking them to issue an Impact partnership from their side.

I decided not to send that email today. Marketplace auto-reject is a routine outcome, not a hard fail; waiting to see what LazyPay comes back with is a better use of attention than firing off a parallel email that probably will not matter.

HealthKart: dropped

HealthKart does have an affiliate program. The "Refer & Earn" link in their footer takes you to a login / signup page. The signup page asks for a 10-digit mobile number with a +91 country code and an OTP. There is no fallback.

Three things I learned in one afternoon

One. Indian BNPL and supplement affiliate programs have systemic China-resident friction. Of the four non-Amazon options I tried, three either required an Indian phone, parked their brand domain, or operated auto-reject for China-resident publisher accounts through brand-side geo rules. Only LazyPay gave me a real email-able contact.

Two. Service agreements tell you more than rejection emails do. A 30-second grep of the MPSA saved me an afternoon of speculative work. Reading terms is unfashionable; reading them when you are blocked is the highest-leverage thing you can do next.

Three. Document dead ends as well as successes. I updated AFFILIATE-APPLICATION-GUIDE.md to record everything I learned today: the actual signup URL variants I tested, the actual domain that was a placeholder, the actual signup page that wanted an Indian number. The next time I open that file, those four hours of trial and error are not lost.

Outlook

I will hear back from LazyPay in one to four weeks, if at all. If they come back with a real path I will run it; if not, I will add two words to this post — "and got nothing" — and call P2 a structural dead end rather than keep grinding.

The thing I keep coming back to is that LazyPay / Simpl / HealthKart are not the business. Amazon Associates is. The reason to keep trying the others is upside, not foundation. Knowing where to stop trying is, in a small way, also part of building.

You can ship a similar small affiliate site from anywhere in the world if Amazon is your monetization. Where you live starts to matter the moment you try to add a second program.

Tim Yx runs proteinpayment.com, an English-language affiliate site covering BNPL, EMI, and other payment methods for protein supplement buyers across India, Indonesia, Brazil, and Mexico.

How I shipped a free Next.js calculator with Amazon affiliate links — and what 180 days of tracking taught me

Tim — Mon, 03 Aug 2026 07:50:22 +0000

Twelve days into a free sourdough hydration calculator monetized through Amazon Associates, the data shows 0 clicks and an average position of 43.3. Here is what is actually working, what I should have done on day one, and what surprised me most about Cloudflare's defaults in 2026.

Twelve days ago, I shipped a free tool at sourdough-hydrationcalculator.com. The plan in theory was simple: a useful calculator for home bakers, monetized through Amazon affiliate links. No signup, no database, no server.

What I did not plan for was the clock. Amazon Associates closes new accounts if they do not hit three qualifying sales within 180 days of sign-up. So somewhere between curiosity and a deploy script, my hobby project became a small business with a deadline.

This is not a "I made money" post. The honest number is zero sales so far. What I can share is everything I learned about building a Next.js calculator, wiring affiliate links correctly, and instrumenting the early data so I can see what is actually working.

The Next.js choice

The calculator does exactly one thing: take a flour weight and return water, salt, and starter amounts. It also shows a hydration chart from 50% to 100%.

I considered three stacks:

A React SPA with a backend
Hugo or Astro, content-first
Next.js with static export

Static export won because the SEO value of content lives with the marketing, and content marketing for niche calculators is mostly blog posts, FAQs, and comparison tables. Static export lets every blog post be server-rendered HTML, which is what Google prefers and what AI search tools can read reliably.

The build is next build with output: export, deployed to Cloudflare Pages on every commit to main. There is no server, no runtime cost, no auth flow to maintain. That choice reflects a bet: most of my real work over the next 180 days will not be writing code, it will be writing articles and watching what ranks.

The 180-day Amazon clock

This is the part I did not appreciate when I started.

Amazon's Associates Terms require three qualifying sales within 180 days of sign-up. If you do not hit that threshold, the account is closed. There is no extension, and re-applying is harder than the first time.

Two practical implications.

First, the clock starts the moment you sign up, not when traffic arrives. If you spend 90 days building before launching, you have 90 days of runway left, not 180.

Second, the model is not "monetize after you have an audience." It is "monetize from page one." Your first six months are paid existence, with traffic as the cost of admission.

If you want optionality, you can build the site without affiliate links for the first month, do real SEO work, then add Amazon once you have a page-one ranking to point at. That gives you the freedom to launch earlier.

I shipped the calculator with affiliate links already in place. I would not do that again.

How I structured affiliate links

Because Amazon is strict about disclosure and Google penalizes schemes, I did four things that I think are non-negotiable.

First, a single disclosure prominently visible on every page that has an affiliate link. The exact sentence, "As an Amazon Associate, we earn from qualifying purchases," is required and cannot be edited. Add a small note that lowers anxiety, such as "Prices stay the same for you." Most readers who see Amazon links wonder whether they will pay more if they use yours. They will not. That sentence is worth more than it sounds.

Second, every Amazon link carries rel="sponsored nofollow noopener noreferrer". The sponsored value is the Google-required attribute for paid placements. nofollow keeps link equity inside the site. noopener noreferrer is basic security.

Third, no more than two affiliate links per blog post. A page that recommends six kitchen tools reads like a shopping list. A page that recommends the two tools relevant to the article reads like advice. Google's "thin affiliate" filter targets the first pattern, and so do readers.

Fourth, links go only on pages where the product genuinely fits the article. Recipes and explainers that never benefited from a product do not get a link.

What twelve days of data actually shows

From Google Search Console, twelve days after launch:

24 pages are tracked
10 are indexed
14 are not yet indexed
Average position across all impressions: 43.3
Clicks: 0

The 0 clicks and 43.3 average are not failures. They mean the site is on page four or five of Google for whatever queries it ranks. No human clicks through page four.

The reason for the slow start turned out to be infrastructure, not content. Cloudflare's Bot Fight Mode was quietly serving Googlebot an HTML 403 page where browsers saw a sitemap. One toggle in Cloudflare's Security → Bots fixed it.

This is the part I want to flag for anyone shipping a free site in 2026. Cloudflare's default protections now actively block verified search engine crawlers, even ones that have been verified for years. If your new site has 0 clicks and a low impression count, check this before you spend hours on content audits.

Three things I would do differently

Add llms.txt on day one, not day thirty. ChatGPT, Perplexity, and Google's AI Overviews crawl the web looking for citation candidates. llms.txt is a small plain-text file at the root that says, "Here is what this site is, here are its most citable pages." It costs about twenty minutes to write. If you have any hope of being cited by an AI answer in the next six months, the file is a cheap edge.

Use GSC's URL Inspection to request indexing, rather than wait for the sitemap. Google Search Console has a "Request Indexing" button when you paste a URL into the URL Inspection tool. It is faster than waiting for the sitemap to be processed, which can take one to three days. There is a daily limit of about ten requests per site, so for a new site with thirty pages, request the five most important pages on day one, then the next five the next day, and so on.

Do not change settings you do not understand. This one is mostly a self-note. I once changed emoji icons to text badges because the emoji looked broken in some browsers. The user (me, in this case) wanted emoji back. The change wasted two deploys and added no value. The lesson for shipping early is to leave visuals alone once they work. Optimize when there is a real signal that they need to change.

What comes next

The next four weeks:

Request indexing for the six articles that were crawled on the old version of the site
Wait for Google to re-evaluate with the new schema, FAQ, and citation changes in place
Publish five to ten long-tail articles that match specific home-baking queries
Measure clicks, not impressions

If the calculator ever does its job, the result will be a small number of Amazon sales, a steady stream of home bakers finding the hydration ratio they actually need, and a real-time lesson on what AI-era SEO looks like in 2026.

For now, the project is in its quiet phase. The tool is at sourdough-hydrationcalculator.com, and the only thing I am working on is making sure Google can see it.

If you're shipping a similar small site, the early moves that mattered most were the boring ones: real schema on real pages, real citations on real articles, a sitemap that does not lie. The flashy stuff is optional. The plumbing is not.

What an SEO Audit Taught Me About Shipping a Next.js Static Site

Tim — Sat, 01 Aug 2026 13:26:42 +0000

I ran an SEO audit on my Next.js 14 static site and found 20+ issues. Here is exactly what I fixed, what broke, and what actually mattered.

Last month I shipped a free tool called Sourdough Hydration Calculator. It is a Next.js 14 static export hosted on Cloudflare Pages. No database, no signup, no server. Just a calculator, a chart, and a bunch of blog posts.

Then I ran an SEO audit on it. The score was not terrible, but the report was full of small warnings: missing favicon, descriptions too long, keywords too long, no canonical on blog listing pages, no author markup, headings that jumped levels, and a few citations that did not exist yet.

None of these issues were going to kill the site. Together, though, they made the whole thing feel unfinished. So I fixed them one by one. This post is what I learned, including the mistakes I made along the way.

Start with the boring metadata

The first round of fixes was pure housekeeping:

Meta descriptions under 160 characters
Titles under 60 characters
Keywords meta under 100 characters (yes, Google ignores it, but Bing still reads it)
A real favicon in SVG, 32px PNG, and 180px PNG
Canonical tags on every page, including paginated blog pages
Open Graph and Twitter cards with a consistent site name

The pattern that worked for every blog post was simple:


js
export const metadata = {
  title: "Sourdough Bulk Fermentation: Timing and Temperature",
  description: "When to end sourdough bulk fermentation: 5 visual cues, temperature timing, and common mistakes.",
  alternates: {
    canonical: "https://sourdough-hydrationcalculator.com/blog/bulk-fermentation/",
  },
  openGraph: {
    siteName: "SourdoughCalc",
    type: "article",
    url: "https://sourdough-hydrationcalculator.com/blog/bulk-fermentation/",
  },
};
Nothing clever. The audit score moved from warnings to clean because the page finally said the same thing everywhere: this is one page, this is its name, this is what it is about.
Remove fake trust signals
My first version of the homepage included structured data with a rating:

{
  "@type": "SoftwareApplication",
  "name": "SourdoughCalc",
  "aggregateRating": {
    "ratingValue": "4.8",
    "ratingCount": "127"
  }
}

I had no real reviews behind those numbers. That is the kind of thing Google can penalize as deceptive, and it is not worth the risk. I deleted the aggregate rating and kept the rest of the schema. If we ever collect real reviews, we can add them back honestly.
Broken images hide in plain sight
The audit did not find this, but a simple script did: 19 blog posts referenced PNG hero images that did not exist. The <picture> element pointed to a WebP source, so modern browsers never requested the PNG. The page looked fine.
The problem was structured data. Article schema pointed to the missing PNG, which meant search engines and AI crawlers were told to look at an image that returned 404.
The fix was mechanical:

image: "/images/blog/bulk-fermentation.webp"

Lesson: test the URLs that only machines read, not just the ones humans see.
My sitemap broke in a boring way
I wanted to update every <lastmod> in the sitemap to today's date. I wrote a regex replacement in PowerShell and pushed it without looking at the output carefully enough.
The replacement produced this:

<loc>https://sourdough-hydrationcalculator.com/blog/...</loc>
$12026-08-01</lastmod>

Every single lastmod lost its opening tag and gained a literal $1. The file was still deployed. Nothing crashed. It just became invalid, quietly.
The lesson is obvious in hindsight: after any batch edit, parse the file, count the records, and print a sample. I now validate the sitemap with an XML parser before every deploy.
Structured data is not just for articles
Blog posts already had Article schema. The blog listing page had none. So I added a Blog entity with author, publisher, dateModified, and a list of the posts on that page:

{
  "@context": "https://schema.org",
  "@type": "Blog",
  "name": "Sourdough Baking Blog",
  "url": "https://sourdough-hydrationcalculator.com/blog/",
  "author": {
    "@type": "Organization",
    "name": "SourdoughCalc",
    "url": "https://sourdough-hydrationcalculator.com/"
  },
  "dateModified": "2026-08-01"
}

The same page also needed a byline. Anonymous content is cited less, and the fix was one line: By SourdoughCalc Team. We did not invent a fake person; an honest team attribution is enough.
Headings should not jump levels
The audit complained about a heading jump from H2 to H4. The culprit was the footer: it used <h4> for column titles. On article pages that was fine, but on listing pages with only H1 and H2, the footer created a skipped level.
Changing four footer headings from h4 to h3 fixed the whole site:

<h3 class="text-brand-wheat mb-3">Quick Links</h3>

Small change, site-wide effect.
Citations helped more than I expected
The audit kept saying "No citations or quotations". I did not want to link to competitors at the top of articles, so I added a small Sources section at the bottom of the posts that made scientific or technical claims.
For nutrition and digestion posts, I used real PubMed studies published in 2026. For baking technique posts, I linked to established sources like King Arthur Baking and The Perfect Loaf.
Two rules:
Verify every URL returns 200 before adding it
Add rel="nofollow noopener" so citations do not leak link equity
The result: the citations are visible to readers and AI tools, but Google does not treat them as endorsements.
Headings that read like questions get cited more
This one is not about rankings, it is about AI visibility. Sections titled What Temperature Should You Bake Sourdough? are easier for AI answers to pull from than sections titled Bake Day (Evening).
I reframed key H2s across the blog:
Why Is Focaccia Perfect for Beginners?
How Do You Fix Sourdough Pizza Problems?
Does Hydration Affect the Crust?
It is a small content change, but it makes every post more extractable.
Add a file for AI crawlers
robots.txt tells search engines what they may crawl. llms.txt is a younger idea: a plain text file that tells AI tools what your site is and which pages matter.

# SourdoughCalc

> Free sourdough hydration calculator for home bakers.

https://sourdough-hydrationcalculator.com/
https://sourdough-hydrationcalculator.com/blog/

Adoption is still low, but the file costs nothing and gives AI crawlers a clean starting point.
Verify the deployed page, not just the build
My biggest mistake was trusting my own verification too early.
After one deploy, I fetched the homepage and wrote a quick regex to check the affiliate links. The regex returned the URL but not the anchor tag around it, so I concluded that the live site was missing rel attributes and that the deployment was stale.
It was not. The site had been updated; my check was wrong.
Now I wait 15 seconds after a deployment reaches success, fetch the real HTML, and compare exact strings before reporting anything.
What actually mattered
If I ranked the fixes by impact:
1.Fixing broken image URLs in structured data
2.Removing fake aggregate ratings
3.Adding canonical tags everywhere
4.Making the sitemap valid again
5.Adding honest author and Blog schema
6.Adding real, nofollow citations
7.Reframing headings as questions
None of these were dramatic. Most were boring. Together they turned a site that looked finished into one that was actually finished.
The tool itself is still free and still does one thing: Sourdough Hydration Calculator. If you are shipping a static site, I hope this list saves you a few audits.

How I Built a Sourdough Hydration Calculator with Next.js 14 (Free Tool)

Tim — Tue, 28 Jul 2026 17:10:48 +0000

Why I built it

Every sourdough recipe online gives you hydration as a percentage, but most home bakers have no idea what that means in practice. You see "75% hydration" and think "OK, 75% of what?" I watched my partner struggle with this for months before I finally wrote a calculator that shows the actual grams of water, flour, and starter to weigh out.

The result is a free, no-signup tool at Sourdough Hydration Calculator. This post walks through the math, the tech stack, and a few decisions that made the project work on a $0/month budget.

The math: baker's percentages explained

Baker's percentages are simple once you see them as ratios. Every ingredient is expressed as a percentage of the total flour weight:

Hydration % = (water weight) / (flour weight) * 100

So a 75% hydration dough has 75g of water for every 100g of flour. Easy.

The wrinkle is that sourdough starter is 50% flour and 50% water. A 100g portion of starter contributes 50g of each to your dough. The calculator handles this automatically:


typescript
const water = flour * (hydration / 100);
const saltGrams = flour * (salt / 100);
const totalStarter = flour * (starter / 100);
const starterFlour = totalStarter * 0.5;
const starterWater = totalStarter * 0.5;
const flourToAdd = flour - starterFlour;
const waterToAdd = water - starterWater;
const totalDough = flourToAdd + waterToAdd + saltGrams + totalStarter;

The output breaks out each ingredient in grams, plus a total dough weight. Six preset hydration levels (65% bagels through 100% ciabatta) handle the common cases.

Tech stack
Next.js 14 (App Router, static export)
TypeScript (strict mode)
Tailwind CSS 3.4
Cloudflare Pages (free hosting)
Plausible Analytics (privacy-friendly, no cookies)
Google Search Console + Bing Webmaster Tools (verification + indexing)
Total monthly cost: $0.
Architecture highlights
Client-side only. No backend, no database, no API. The whole site is a static export. This keeps the bundle small (87 KB shared JS, 19 KB CSS) and the hosting free.
Optimized for Core Web Vitals. Every blog post is pre-rendered at build time. The biggest image (the 1200x630 hero) is converted to WebP at 122 KB. Inline critical CSS via Tailwind.
SEO from day one. Every page has:
Sitemap entry in sitemap.xml
JSON-LD Article schema (auto-generated via shared component)
Open Graph + Twitter Card meta tags
Canonical URL pointing to itself
Internal links between related posts
Two output modes. Grams for serious bakers, plus a "cups + tsp" toggle for casual bakers. Salt is shown in teaspoons at small quantities because 10g of salt as "0.04 cups" is useless in practice.
Why Next.js 14 specifically
I started with vanilla React + Vite, but Next.js's static export (output: 'export') gave me:
File-based routing (no React Router setup)
TypeScript + ESLint out of the box
Built-in CSS modules + Tailwind integration
Image optimization (even for static export)
One-command deploy to Cloudflare Pages
The catch: next/image doesn't work with output: 'export' by default. Setting images.unoptimized: true disables it. The trade-off is acceptable for a small site.

What's free, what's not

| Component | Provider | Cost |
|---|---|---|
| Hosting | Cloudflare Pages | $0 |
| Domain | Cloudflare Registrar | ~$10/year |
| Analytics | Plausible | $0 (self-host) or $9/month |
| Image gen | Agnes AI | Pay per image (~$0.05 each) |
| Email | Cloudflare Email Routing | $0 |
| Newsletter | (not using yet) | TBD |

Total fixed cost: domain only.
What I'd do differently
If I started over:
Skip Next.js, use Astro. Faster builds, smaller output, less JS shipped.
Use Cloudflare D1 for user accounts (free tier) instead of client-only.
Add Web Vitals tracking in CI to catch regressions.
Try it
The free Sourdough Hydration Calculator is live. Six preset hydration levels (Bagel, Sandwich, Classic, Country, Open Crumb, Ciabatta) cover most home-baking needs. No signup, no tracking beyond aggregate page views, no email collection.
For the underlying math, see the open baker's percentage guide on the same site. For a quick sanity check, try entering 500g flour + 75% hydration + 20% starter — you should get 287.5g water-to-add, 10g salt, and 850g total dough (before bake loss).
If you bake, I'd love to hear whether the defaults work for your kitchen. If you don't bake, I hope the math section was at least interesting.