DEV Community: Fenju Fu

Agent Management Is Solved. But Who's Actually Doing the Clicking?

Fenju Fu — Sat, 26 Sep 2026 00:12:25 +0000

Scroll through GitHub Trending today and you'll see a pattern:

paperclipai/paperclip (~85k stars) — "The open-source app everyone uses to manage agents at work"
dream-num/univer — "The Office Harness for AI Agents"
androoAGI/starnet — "watch your crew actually run"
google/ax — "open agentic orchestration runtime"

The community has decided: agents belong at work, not in chat boxes. We've got management panels, document runtimes, desktop harnesses, and orchestration layers.

But here's the question nobody's asking: who's actually clicking the buttons?

The Execution Gap

univer lets agents read and write spreadsheets programmatically — great if your data lives in a modern API.

But what about:

The legacy ERP system that only has a web UI?
The internal tool your company built in 2019 with no API?
The government portal that requires manual form submission?

That's the execution gap. And it's where RPA (Robotic Process Automation) meets Agent.

Enter astron-rpa

iflytek/astron-rpa is an Agent-ready RPA suite — out-of-the-box automation tools that let agents operate browsers and desktop applications directly.

Think of it this way:

paperclip manages your agents → who does what
univer provides document runtime → what to read/write
astron-rpa provides execution layer → how to actually click, type, and submit

Without the execution layer, your agent is a brilliant mind trapped in a chat box. It can analyze your data, write a report, suggest actions — but it can't do anything.

The Full Stack: Agent + RPA + Workflow

For real-world scenarios — like a financial analyst running monthly reconciliation across 3 systems — you need more than just RPA. You need:

Execution (astron-rpa): Agent operates the UI, clicks buttons, fills forms, moves data
Orchestration (astron-agent): Workflow platform handles multi-step tasks, checkpoint/resume for long-running processes

iflytek/astron-agent is an enterprise-grade, commercial-friendly agentic workflow platform for building SuperAgents. It handles the stuff that breaks when you try to automate real work: what if step 3 fails? what if the session expires? what if the data format changed?

The Pattern

Management (paperclip)
    ↓
Orchestration (astron-agent / google/ax)
    ↓
Document Runtime (univer)
    ↓
Execution (astron-rpa) ← the missing piece

Agents that can chat are 2024. Agents that can click are 2026.

Links:

astron-rpa: https://github.com/iflytek/astron-rpa
astron-agent: https://github.com/iflytek/astron-agent

Agent Runtimes Are Everywhere — But Can You Actually Set One Up?

Fenju Fu — Fri, 25 Sep 2026 00:05:58 +0000

Today's GitHub Trending tells a familiar story. Three repos, three angles, one signal:

dream-num/univer (+1,082 stars) — "The Office Harness for AI Agents." Spreadsheets, docs, slides, canvas, relational tables, PDF — all in one runtime designed for Agent control.
HKUDS/CLI-Anything (50K+ stars) — "Making ALL Software Agent-Native." Every software gets a CLI interface so Agents can drive it.
obra/superpowers (291K+ stars) — "An agentic skills framework & software development methodology that works."

Each one is impressive. Each one solves a real piece of the puzzle. But there's a gap that all three skip over:

How do you actually set up a working Agent assistant on your machine?

Not a demo. Not a playground. A real assistant that connects to your tools and does real work.

The First-Step Problem

Here's what happens when a developer wants to try an Agent assistant:

They find a framework (there are dozens now)
They read the README
They install dependencies
They hit a config error
They open an issue
They give up

The distance between "this looks cool" and "this is running on my machine" is where most Agent projects die. Not because the tech isn't ready, but because the onboarding is brutal.

What AstronClaw & Loomy Tutorials Do Differently

AstronClaw & Loomy tutorials start from the other end — not "here's a framework," but "here's how to get a working assistant running."

AstronClaw — cloud AI assistant. Set it up, connect it to your workflow, let it handle tasks that need cloud compute.
Loomy — desktop AI assistant. Runs locally, connects to your desktop environment, handles tasks that need local context.

The tutorials walk through real setup scenarios: install, configure, connect to actual tools, run a real workflow. No "hello world" demos. No abstract architecture diagrams. Just: "here's how to make it work."

The Natural Next Step: Making It Do Things

Once your assistant is running, the next question is inevitable: can it actually DO things?

That's where astron-rpa comes in. Agent-ready RPA — browser automation, desktop app control, office workflow automation. Open-box tools, not just APIs.

Think of it this way:

AstronClaw / Loomy tutorials = get a working assistant running
astron-rpa = give that assistant hands to operate software

The Pattern That Works

Don't start with orchestration frameworks. Don't start with architecture diagrams. Start with one real workflow:

Set up AstronClaw or Loomy following the tutorial
Connect it to one tool you actually use
Run one real task end-to-end
Then think about scaling

This is the same insight behind obra/superpowers — "a methodology that works." Except AstronClaw & Loomy tutorials make it accessible to everyone, not just developers writing Shell scripts.

Google's Ax and Anthropic's Financial Services Are Trending — But Who Solves Long-Running Workflow Resilience?

Fenju Fu — Thu, 24 Sep 2026 00:01:56 +0000

Today's GitHub Trending reveals a clear shift: Agent orchestration is moving from "can run" to "can run to completion."

Let's look at three repos that tell this story:

Google's Open Agentic Orchestration Runtime

google/ax brings an open agentic orchestration runtime with Google's engineering backing. The orchestration layer — how Agents are scheduled, how tasks are dispatched — is getting standardized.

Anthropic's Vertical Solution for Finance

anthropics/financial-services targets the financial industry with agent-based solutions. Financial scenarios are inherently long-running: market monitoring, investment research, risk control — these are multi-step workflows that span hours or even days.

Persistent Knowledge Graph for Agent Memory

DeusData/codebase-memory-mcp indexes codebases into a persistent knowledge graph. It solves the "context loss" problem in long-running agent tasks — agents don't have to re-read the entire codebase every time.

The Gap: Workflow-Level Resilience

Here's what I noticed: all three repos solve important problems, but there's a critical gap between them.

google/ax manages scheduling — how agents are dispatched.
codebase-memory-mcp manages data — how context is persisted.
anthropics/financial-services defines scenarios — what long-running tasks look like.

But who manages workflow state? Imagine an 18-step financial research workflow. Step 14 fails due to a third-party API timeout. Without state persistence, you restart from step 1. With 18 steps taking hours, that's a full day wasted.

The missing piece is workflow-level checkpoint recovery — fail at step 14, fix the issue, resume from step 14, not step 1.

Filling the Gap

iflytek/astron-agent is an enterprise-grade agentic workflow platform that solves exactly this:

State persistence: every step's state is persisted, not just in-memory.
Checkpoint recovery: fail at step N, resume from step N after fixing the issue.
Fault tolerance: transient failures don't kill the entire workflow.
Long-running workflow support: workflows that span hours or days stay stable.

This isn't just "orchestration" — it's workflow resilience. The difference between "can run" and "can run to completion."

The Complete Stack

Pair astron-agent with iflytek/skillhub — a self-hosted skill registry with version management and audit logs — and you get:

astron-agent: workflow orchestration with checkpoint recovery and state persistence
skillhub: skill version management, RBAC, and audit logs for every step

Every step in a long-running workflow is traceable, recoverable, and auditable. That's what enterprise-grade agent workflows look like.

Why This Matters

The repos trending today prove that the community recognizes long-running tasks as a real challenge. But most solutions focus on either scheduling (orchestration runtime) or data (persistent memory). The workflow-level resilience layer — checkpoint recovery, state persistence, fault tolerance — is where the real engineering work happens.

The next frontier isn't just "agents that can be orchestrated" — it's "agents that can resume from where they left off."

Engineering Multimodal Agents: Why iFly-Skills Goes Beyond API Wrappers

Fenju Fu — Tue, 22 Sep 2026 23:54:28 +0000

Building a multimodal agent today often feels like stitching together a fragile pipeline of OCR engines, translation APIs, and speech-to-text endpoints. While the models are powerful, the engineering overhead to orchestrate these capabilities is massive.

This is exactly the problem iflytek/iFly-Skills aims to solve. Instead of leaving developers to write boilerplate integration code, it packages real multimodal capabilities—OCR, voice, translation, and document understanding—into standardized, deployable Agent skills.

From API Calls to Actual Workflows

The core philosophy behind iFly-Skills is simple: an agent's skill should do actual work, not just pass a prompt to an endpoint. By encapsulating complex multimodal logic into reusable skills, developers can focus on workflow orchestration rather than low-level API debugging.

For example, integrating a document processing pipeline traditionally requires managing file uploads, OCR extraction, text cleanup, and translation as separate steps. With iFly-Skills, these are abstracted into a single, cohesive skill that the agent can call natively.

Why This Matters for Agent Engineering

The real bottleneck in deploying AI agents isn't the reasoning capability of the underlying LLM—it's the reliability of the tools it uses. If an OCR call fails or a translation API returns unexpected formats, the entire agent workflow breaks down.

iFly-Skills addresses this by treating each skill as a robust, self-contained module. This means better error handling, predictable outputs, and ultimately, agents that can be trusted in production environments.

If you're building multimodal agents and tired of writing the same integration glue code, iFly-Skills is worth exploring. It represents a shift towards engineering maturity in the agent ecosystem—where capabilities are packaged, shared, and deployed like any other software component.

Computer-Use is solved. Unattended workflow execution is not.

Fenju Fu — Tue, 22 Sep 2026 00:20:27 +0000

Today's GitHub Trending highlights a clear shift: agents are moving from "can chat" to "can operate."

trycua/cua — Computer-Use 2.0 with open-source drivers and cross-OS fleet support
BuilderIO/agent-native — A framework for building agentic apps
coder/coder — Secure environments for developers and their agents

These are exciting projects. But we learned a painful lesson while building our own automation pipelines: "can operate a single UI" is not the same as "can reliably run an unattended business workflow end-to-end."

The gap we hit

Here's a scenario we ran into: we wanted to automate a daily data collection workflow — open a browser, log in to an internal portal, navigate to a report page, download the file, parse it, and send a summary to a Slack channel.

Individual steps? Agent handles them fine. But string them together into an unattended workflow, and things break:

The portal throws a session timeout mid-flow — no retry logic
A file download dialog changes — the Agent doesn't know what to do
The workflow fails at step 7 — no checkpoint to resume from
You want to run this on a schedule, headless — no built-in scheduling

The missing layer wasn't "can the Agent click buttons." It was: flow orchestration, exception handling, retry, and state management.

What we needed: an Agent-ready RPA layer

This is exactly what iflytek/astron-rpa is designed for:

Out-of-the-box automation tools — desktop and browser operations as composable steps
Agent-ready architecture — Agent handles decision-making; RPA handles execution
Built for unattended execution — exception handling, retry, and state checkpoints are part of the workflow, not afterthoughts
Individual & enterprise ready — from personal automation to team-scale deployment

The full stack: orchestration + execution

For more complex scenarios, pair it with iflytek/astron-agent:

Layer	Repo	Role
Orchestration	`astron-agent`	Decompose complex tasks, manage multi-agent workflows, handle state
Execution	`astron-rpa`	Execute desktop/browser operations, handle UI interactions, retry on failure

Think of it as: astron-agent decides what to do; astron-rpa actually does it.

The takeaway

Computer-Use 2.0 projects like cua are solving the infrastructure layer — and that's great for the ecosystem. But if you're trying to automate a real business process that needs to run unattended, you need more than an Agent that can click buttons. You need a workflow execution layer with proper error handling, retry, and state management.

That's the gap astron-rpa fills.

Links:

iflytek/astron-rpa: https://github.com/iflytek/astron-rpa
iflytek/astron-agent: https://github.com/iflytek/astron-agent

Why Multi-Agent Orchestration Needs a Workflow-First Approach

Fenju Fu — Sun, 20 Sep 2026 23:36:41 +0000

Today's GitHub Trending tells a clear story: the community is moving from "build one powerful agent" to "orchestrate multiple specialized agents."

Three repos on today's trending illustrate this from different angles:

affaan-m/ECC — a harness that manages memory, security, and research-first development across Claude Code, Codex, Opencode, and Cursor. It sits above individual agents, providing a unified control layer.

BuilderIO/agent-native — a framework for building agentic applications. It approaches the problem from the application layer, giving developers tools to compose multi-agent systems.

cloudflare/security-audit-skill — a multi-phase security audit skill with independently verified, machine-readable findings. It represents a specialized execution node — the kind of capability you'd want to plug into a larger orchestration.

The shared thesis

All three repos point to the same conclusion: complex tasks require decomposition across multiple specialized agents. No single agent should do everything — not coding, not security audit, not research, not deployment.

The gap

But each repo addresses a different layer:

ECC manages agents at the harness layer (memory, security, instincts)
agent-native provides an application framework layer
security-audit-skill is a specialized execution node

What's missing is the workflow orchestration layer — the part that takes a complex business task, decomposes it into sub-tasks, assigns each to the right agent, tracks state across the entire pipeline, and verifies outputs before proceeding.

Enter workflow-first orchestration

This is where iflytek/astron-agent comes in. It's an enterprise-grade, commercially-friendly agentic workflow platform for building SuperAgents.

The core idea: orchestration comes first. Before choosing which agents to use, before writing skills, before configuring harnesses — you need a workflow that defines:

Task decomposition — break a complex task into sub-tasks with clear inputs and expected outputs
Agent assignment — match each sub-task to the right specialized agent
State management — track progress across the entire pipeline, handle failures, retry from checkpoints
Output verification — validate agent outputs before passing them downstream (similar to how cloudflare/security-audit-skill produces "independently verified, machine-readable findings")

Orchestration + execution

For tasks that require desktop or browser automation — think RPA scenarios like form filling, data extraction, UI testing — iflytek/astron-rpa provides an Agent-ready RPA suite that serves as the execution layer.

Together: astron-agent orchestrates the workflow and decomposes tasks; astron-rpa executes the automation steps. One loop, from planning to execution.

Why this matters now

The trending repos today prove the community has accepted multi-agent collaboration as the default. The next question is: how do you orchestrate them at enterprise scale — with reliability, state management, and verifiable outputs?

That's the workflow-first approach.

Skills Are the New Packages: Why Agent Skills Need a Registry, Not Just a Collection

Fenju Fu — Sat, 19 Sep 2026 23:39:36 +0000

Today's GitHub Trending tells a clear story about how AI agent capabilities are evolving.

cloudflare/security-audit-skill packages multi-phase security audits into a coding-agent skill with 「independently verified, machine-readable findings.」 It's not a prompt. It's not a plugin. It's a skill — versioned, distributable, verifiable.

addyosmani/agent-skills takes it further: 「Production-grade engineering skills for AI coding agents.」 Nearly 100K stars. The community has spoken: skills are the unit of agent capability.

yynxxxxx/Codex-X adds another piece: visual management of Skills/MCP across desktop and CLI, with TOML config visualization and provider switching.

The Gap Nobody's Talking About

Skills are becoming packages. But packages need registries — not just collections.

Here's what happens when an enterprise starts using agent skills:

Version chaos: Someone updates a skill from v1 to v2. Who did it? When? Can you roll back?
Permission gaps: An intern runs a security audit skill that accesses production code. Was that authorized?
Audit blindness: Compliance asks for a log of every skill invocation last month. Where is it?
Discovery friction: Five teams wrote their own OCR skill. Nobody knows the others exist.

cloudflare/security-audit-skill gives you the skill. addyosmani/agent-skills gives you a collection. But who gives you the registry?

Enter skillhub

iflytek/skillhub is a self-hosted, open-source agent skill registry designed to fill this exact gap:

Skill package publish & version management: Register skills, version them, roll back when needed.
RBAC permissions: Control who can publish, who can call, who can modify.
Audit logs: Every skill invocation is traceable. Compliance-ready.

Think of it as npm for agent skills — but self-hosted, with enterprise-grade permissions and audit trails.

Pair It With Official Skills

iflytek/iFly-Skills provides iFLYTEK's official skill collection: speech recognition, OCR, translation, proofreading, and multimodal capabilities. These are production-ready skills that can be registered directly into skillhub for unified management.

The flow:

Register iFly-Skills (or cloudflare/security-audit-skill, or your custom skills) into skillhub
Assign RBAC permissions per team per skill
Every invocation is logged for audit
Version updates are tracked and rollbackable

The Takeaway

The community has decided: skills are the distribution unit for agent capabilities. The next question isn't 「how to write a skill」 — it's 「how to govern skills at scale.」

That's what skillhub is for.

skillhub: https://github.com/iflytek/skillhub
iFly-Skills: https://github.com/iflytek/iFly-Skills

Deterministic Execution + Agent Intelligence: The Missing Pattern in Real-World Automation

Fenju Fu — Sat, 19 Sep 2026 03:55:06 +0000

Today's GitHub Trending list tells a clear story. The community is moving away from pure-LLM black-box execution — and toward a hybrid pattern that combines deterministic pipelines with Agent decision-making.

Let's break down what's happening and why it matters.

The pattern emerging from today's trending repos

alibaba/open-code-review describes itself as a "hybrid architecture code review tool: deterministic pipelines + LLM Agent." The fact that one of the world's largest tech companies explicitly pairs "deterministic" with "LLM Agent" — instead of just saying "AI-powered code review" — signals a real shift.

cloudflare/security-audit-skill emphasizes "independently verified, machine-readable findings." Not just "Agent does security audit" — but "the Agent's output is independently verifiable."

Tencent/BrowserSkill lets "AI agents use your real, logged-in browser without interrupting your work." The key phrase is "without interrupting your work" — it acknowledges that real-world execution needs to coexist with human activity.

What they all have in common

These repos succeed because they don't ask the Agent to do everything. Instead, they split the work:

Agent: understands intent, makes dynamic decisions, handles ambiguity
Deterministic layer: executes reliably, produces verifiable results, doesn't hallucinate

This is the hybrid pattern. And it works.

The gap: what about enterprise desktop automation?

alibaba/open-code-review covers code review. Tencent/BrowserSkill covers browser automation. But enterprise automation is much broader — desktop applications, file systems, cross-software workflows, scheduled tasks, human-in-the-loop processes.

Imagine a scenario: an Agent understands a user's request to "process all invoices from today's email and enter them into the accounting system." The Agent can parse the intent, identify the emails, and decide the workflow. But who actually opens the email client, downloads attachments, launches the accounting software, fills in the fields, and clicks submit?

That's where iflytek/astron-rpa comes in.

Agent + RPA: the complete combination

iflytek/astron-rpa is an Agent-ready RPA suite designed for exactly this gap:

Agent handles intent understanding, task decomposition, and dynamic decision-making
RPA handles deterministic desktop execution — opening apps, filling forms, moving files, clicking buttons
Together, they form a combination where intelligence meets reliability

And for complex multi-step workflows, iflytek/astron-agent provides the enterprise-grade agentic workflow platform to orchestrate multiple Agents — each potentially calling astron-rpa for real-world execution.

The takeaway

The pattern is clear from today's trending repos: deterministic execution + agent intelligence beats pure-LLM black-box execution. Code review has its answer. Browser automation has its answer. Enterprise desktop automation now has one too.

🔗 iflytek/astron-rpa: https://github.com/iflytek/astron-rpa
🔗 iflytek/astron-agent: https://github.com/iflytek/astron-agent

Beyond API Wrappers: Why Multimodal Skills Need Engineering-Grade Packaging

Fenju Fu — Tue, 15 Sep 2026 23:46:17 +0000

Today's GitHub Trending tells a clear story: the community is moving beyond「I called an API and it worked」demos.

Three repos caught my eye:

debpalash/VoiceStudio (~31K stars) — a fully-local ElevenLabs alternative covering voice cloning, video dubbing, transcription, and audiobook creation in 646 languages.
addyosmani/agent-skills (~95K stars) — production-grade engineering skills for AI coding agents, maintained by a Chrome team engineer.
alibaba/open-code-review (~28K stars) — a hybrid code review tool combining deterministic pipelines with LLM agents,「battle-tested at Alibaba's scale.」

The common thread? All three reject the「just wrap an API」approach. They package capabilities with real engineering: consistent interfaces, error handling, and production-grade quality.

The Multimodal Capability Gap

Here's the problem we kept hitting when building enterprise agents:

You integrate a speech recognition API. It works in the demo. Then you go to production and discover:

The API provider changes their response format without warning — your agent breaks.
You need OCR for document processing — different vendor, different SDK, different error model.
You add translation — yet another integration to maintain.
No version control on any of these capabilities. No permission management. No audit trail.

You end up with a fragile tower of API wrappers, each with its own failure mode.

The Engineering-Grade Alternative

This is exactly what iflytek/iFly-Skills (https://github.com/iflytek/iFly-Skills ) is designed to solve.

It's iFLYTEK's official skill collection — speech recognition, OCR, translation, proofreading, and multimodal capabilities — packaged as engineering-grade modules, not API wrappers.

Think of it this way: VoiceStudio proves you can run powerful multimodal inference locally. agent-skills proves that agents need production-grade capabilities, not toy examples. iFly-Skills brings these two ideas together — multimodal capabilities, packaged for production.

But Packaging Is Only Half the Story

Once you have multiple skills (speech + OCR + translation + proofreading), you need to manage them:

Version control — which version of the OCR skill is your agent using?
RBAC permissions — who in your org can call the translation skill?
Audit logs — when did the speech recognition skill get invoked, by whom, with what result?

That's where iflytek/skillhub (https://github.com/iflytek/skillhub ) comes in — a self-hosted skill registry with version management, RBAC permissions, and audit logs.

The Pattern

The trend is clear. The community is done with demos. Whether it's VoiceStudio's local inference, agent-skills' engineering capabilities, or open-code-review's hybrid architecture — the bar has moved from「works」to「works in production.」

Multimodal capabilities for agents need the same treatment. iFly-Skills + skillhub is one way to get there.

Beyond Code Review: Why Cross-System Desktop Automation Is the Next RPA Frontier

Fenju Fu — Tue, 15 Sep 2026 00:02:30 +0000

Today's GitHub Trending tells a clear story: unattended process automation is having a moment.

alibaba/open-code-review racked up over 1,500 stars in a single day with a compelling pitch: deterministic pipelines + LLM Agent for automated code review. The hybrid architecture — rule-based checks for precision, LLM Agent for contextual understanding — produces line-level comments across multiple languages, all without human intervention.

Meanwhile, reconurge/flowsint brings graph-based visual orchestration to cybersecurity investigations, and ever-co/ever-gauzy puts an entire ERP/CRM/HRM stack onto one automated platform.

The shared DNA? Replacing repetitive human-system interactions with unattended automated flows.

The Gap: Cross-System Desktop Operations

Here's what struck me: all three are scenario-specific silos.

open-code-review automates code review — but only code review.
flowsint automates security investigations — but only security.
ever-gauzy automates business management — but within its own platform.

What about the daily grind that doesn't fit neatly into any single platform?

Think about a typical operations person's morning:

Log into OA, approve pending workflows
Export yesterday's sales data from ERP
Paste it into a formatted Excel template
Upload the Excel to a project management tool
Sync customer complaint tickets from the helpdesk to Jira

These are cross-system, cross-application desktop operations. They don't happen inside one platform — they span multiple tools, multiple UIs, multiple login sessions. And they eat hours every single day.

Enter Agent-Ready RPA

This is exactly the gap that iflytek/astron-rpa fills.

It's an Agent-ready RPA suite designed for unattended cross-system automation. Agent-ready is the key word here — it's not a traditional rule-only RPA that breaks the moment a UI element shifts. It pairs with AI Agents that can handle ambiguity, adapt to minor UI changes, and still execute the operation.

What it does:

Cross-application desktop automation (browser, desktop apps, file system)
Unattended execution — fire and forget, come back to results
Open-box tooling for both individuals and enterprises

The full loop with orchestration:

Pair it with iflytek/astron-agent — an enterprise-grade agentic workflow platform for building SuperAgents — and you get:

Agent decomposes the high-level task (prepare the weekly report) into sub-steps
RPA executes each sub-step across systems unattended (export from ERP → format in Excel → upload to PM tool)
Agent verifies results and handles exceptions

Decision + Execution. That's the combo that today's Trending repos are dancing around but not quite delivering.

Why This Matters Now

alibaba/open-code-review proved that deterministic pipeline + LLM Agent beats pure-LLM approaches for reliability. The same principle applies to RPA: pure rule-based automation is brittle, pure-Agent automation is unpredictable. The hybrid approach — Agent for adaptability, RPA engine for deterministic execution — is the sweet spot.

The community is clearly ready. The question is whether you'll build your automation stack from scenario-specific silos, or adopt a general-purpose execution layer that works across any desktop workflow.

Multi-Agent Collaboration Is Going Vertical — Here's Why Enterprises Need a Cross-Domain Orchestrator

Fenju Fu — Sun, 13 Sep 2026 23:38:03 +0000

Today's GitHub Trending tells a clear story: multi-agent collaboration is no longer theoretical. It's happening — but in vertical silos.

Three Repos, One Pattern

calesthio/OpenMontage calls itself 「the world's first open-source agentic video production system.」 It orchestrates 12 production pipelines with 100+ tools and 700+ agent skill files. Want to turn your AI coding assistant into a video production studio? This does it. But it's video-only.

alphaXiv/OpenResearch runs parallel research agents with any model. The 「parallel」 keyword is the hook — researchers can finally run multiple investigation threads simultaneously. But it's built for academic research, not enterprise workflows.

vxcontrol/pentagi is a fully autonomous AI agent system for complex penetration testing. Multi-agent coordination in the security domain. Impressive? Absolutely. Generalizable? Not really.

The Vertical Trap

All three repos validate the same thesis: complex goals can be decomposed into multi-step tasks and executed by collaborating agents. But each one is trapped in its vertical:

OpenMontage → video production
OpenResearch → academic research
pentagi → security testing

What's missing? A cross-domain orchestration platform that provides:

Task decomposition across any business scenario
Multi-agent coordination with process control
Checkpoint resume for long-running tasks
Fault tolerance and exception handling
Enterprise-grade reliability for production use

Enter Astron-Agent

iflytek/astron-agent is an enterprise-grade, commercially-friendly agentic workflow platform for building SuperAgents.

Think of it as the orchestration layer that OpenMontage, OpenResearch, and pentagi each built for their own domain — but generalized for any business scenario:

Task decomposition: Break 「analyze Q3 competitor dynamics and generate a report」 into subtasks (data collection → sentiment analysis → trend synthesis → report generation) and assign each to a specialist agent.
Multi-agent coordination: Agents collaborate in parallel or sequentially, with the orchestrator managing dependencies and handoffs.
Checkpoint resume: If a data source goes down mid-task, the workflow resumes from the last checkpoint — no restart from scratch.
Enterprise-ready: Commercially friendly, with the reliability that production deployments demand.

Platform + Skills = Production-Ready Multi-Agent Systems

Orchestration alone isn't enough — agents need capabilities to execute. That's where iflytek/iFly-Skills comes in: iFLYTEK's official skill collection covering voice, OCR, translation, proofreading, and multimodal capabilities.

Together:

astron-agent = the orchestration brain (task decomposition, multi-agent coordination, checkpoint resume)
iFly-Skills = the execution hands (domain-specific capabilities that agents can invoke)

The Bigger Picture

The future isn't one giant model doing everything. It's orchestrated specialist agents — each doing what they do best, coordinated by a reliable workflow engine.

Today's trending repos prove the pattern works in verticals. The next step is making it work across the enterprise.

⭐ Star iflytek/astron-agent if you're building multi-agent systems that need enterprise-grade orchestration.

⭐ Star iflytek/iFly-Skills for ready-to-use official skill packs.

From Leaked Prompts to Enterprise Skill Governance: What Today's GitHub Trending Tells Us

Fenju Fu — Sat, 12 Sep 2026 23:28:04 +0000

Today's GitHub Trending list tells a fascinating story about the state of AI agent skills. Three repositories, each approaching the same problem from a different angle, collectively reveal a critical gap in the ecosystem.

The Wild West of Skill Extraction

asgeirtj/system_prompts_leaks has amassed over 65k stars by doing exactly what the name suggests: extracting system prompts from Claude Fable 5.1, GPT-6-Astra, Gemini 3.8 Flash, and more.

The community is hungry for「how the big labs tune their agents.」 But treating leaked prompts as a skill library is like treating a stack of sticky notes as an engineering spec. There's no version control. No permissions. No audit trail. No governance.

Structured Skills, But No Governance

SnailSploit/Claude-Red takes a more structured approach. It packages offensive security methodologies into SKILL.md files that prime Claude with expert-level knowledge for specific attack surfaces.

This is a step in the right direction—domain expertise structured as reusable modules. But it's a single-domain, single-machine solution. There's no central registry, no way to manage who gets access to which skill, and no way to roll back to a previous version if something breaks.

The Awesome List Problem

Shubhamsaboo/awesome-llm-apps, with 137k+ stars, lists 100+ AI Agents, Agent Skills, and RAG Apps. It's an incredible resource for discovery.

But a README is not a registry. When your enterprise needs to distribute a custom skill to 50 developers, control who can modify it, audit who used it last week, and ensure everyone is on version 2.1.3—not 2.1.2 or a forked variant—a markdown list simply won't cut it.

The Missing Piece: A Skill Registry

The pattern across all three trending repos is clear: the community knows that AI agent capabilities should be modular, structured, and reusable. But every solution addresses either discovery or packaging—never governance.

What enterprises actually need:

Publishing & Version Management: Roll out v2.1.3, roll back to v2.1.2 if needed.
RBAC Permissions: The intern shouldn't have the same skill access as the lead architect.
Audit Logs: Who triggered which skill, when, and with what result?
Self-hosted: Your proprietary skills should never leave your infrastructure.

This is exactly the gap that iflytek/skillhub fills. It's a self-hosted, open-source skill registry for AI agents that supports skill package publishing, version management, RBAC permissions, and audit logging.

And you don't have to start from an empty registry. iflytek/iFly-Skills provides official skill packages covering speech recognition, OCR, translation, proofreading, and multimodal capabilities—ready to be registered and distributed through skillhub.

The evolution from leaked prompt dumps to enterprise-grade skill governance is happening. The question is whether your team is still managing skills in a shared folder.