DEV Community: Claudify

The CLAUDE.md mistakes I kept making (and what fixed them)

Claudify — Tue, 11 Aug 2026 15:57:36 +0000

I have been running Claude Code as my daily driver for over a year, and for most of that time my CLAUDE.md was working against me. Not dramatically. Just a steady tax of repeated corrections, forgotten rules, and sessions that got slower and dumber as the day went on.

Here are the mistakes I kept making, and what actually fixed each one.

Mistake 1: Repeating instructions in chat instead of writing them down

For months I typed the same corrections into chat. "Use pnpm, not npm." "Don't touch the migrations folder." "British spelling in docs." Every session, again.

The obvious cost is my time. The less obvious cost is tokens. Every instruction repeated in chat is context spent in that session, and it vanishes when the session ends. An instruction in CLAUDE.md loads once, automatically, in every session, forever.

My rule now: if I have typed the same correction twice, it goes in CLAUDE.md before I type it a third time. This one habit removed most of my daily friction.

Mistake 2: Believing CLAUDE.md instructions are enforcement

They are not. An instruction in CLAUDE.md is a request. The model reads it, usually honours it, and occasionally does not, especially deep into a long session when earlier context has faded.

I learned this when "never commit directly to main" failed on exactly the day it mattered. The fix was realising that Claude Code has two different tools for two different jobs:

CLAUDE.md is for preferences and conventions, things where 95 per cent compliance is fine.
Hooks are for rules, things where 95 per cent is a failure. A PreToolUse hook that inspects the command and exits non-zero blocks the action deterministically. The model cannot forget a hook, because the hook is not the model.

Now anything I would describe as "must never happen" is a hook. Anything I would describe as "prefer this" stays in CLAUDE.md. Sorting my rules into those two buckets took twenty minutes and eliminated a whole class of incidents.

Mistake 3: Letting it read whole files when a slice would do

By default I would say "look at the auth module" and watch it pull in thousands of lines, most of them irrelevant. Every one of those lines occupies context that could hold something useful.

What fixed it: being specific in prompts ("read the validateSession function in auth/session.ts, lines around the token check") and adding a line to CLAUDE.md asking for targeted reads over whole-file reads. Grep first, read the matching region, only expand when needed. Sessions stay sharp noticeably longer.

Mistake 4: One giant session for everything

I used to keep a single session alive all day. Refactor in the morning, debug after lunch, write docs at the end. By the afternoon the model was dragging 100k plus tokens of morning context into every response, and quality dropped in ways that were easy to blame on the model rather than on me.

Fresh sessions per domain beat one long drag every time. Context from the refactor does not just waste space during the docs work, it actively biases it. When I switch task type, I start clean and let CLAUDE.md carry the durable rules across.

Mistake 5: Compacting mid-task

Compaction summarises the conversation so work can continue, and summaries are lossy. Compact in the middle of a delicate change and the details that get dropped are precisely the ones you needed.

The fix is timing. I compact at boundaries: a task finished, a test suite green, a decision recorded. Before compacting I make sure the current state is written somewhere on disk, a notes file or the plan doc, so the resumed session can recover specifics from files rather than from a summary.

Mistake 6: Leaving every MCP server switched on

This one surprised me. Every enabled MCP server injects its full tool definitions into the context on every request, whether or not you use it. I had accumulated a dozen servers and was paying thousands of tokens per request for tools I touched once a month.

Audit yours. Disable anything you have not used in a fortnight. Re-enabling takes seconds when you actually need it, and the reclaimed context goes to your actual code.

Mistake 7: Exploring in the main thread

When I did not know where something lived, I would have the main session search, open files, follow dead ends. All of that exploration, including the dead ends, stayed in context for the rest of the session.

Subagents fixed this. Delegate the exploration ("find where rate limiting is applied and report the file paths and the key function"), and only the answer comes back to the main thread. The wrong turns are discarded with the subagent. The main session stays focused on the actual change.

The pattern underneath all of these

Every mistake above is the same mistake wearing different clothes: treating context as free. It is not. It is the scarcest resource in the whole setup, and almost everything that felt like a model problem turned out to be a context problem I had created myself.

Durable rules in CLAUDE.md. Hard rules in hooks. Targeted reads. Fresh sessions per domain. Compact at boundaries. Trim the MCP list. Delegate exploration.

None of it is clever. All of it compounds.

What is in your CLAUDE.md that you find yourself repeating in chat anyway? I am curious whether others hit the instruction-versus-enforcement wall the same way I did.

I Replaced My Entire Code Review Process with an AI Agent

Claudify — Tue, 17 Mar 2026 00:18:30 +0000

Code reviews were eating 2-3 hours of my day. Not writing code — reviewing it. Reading diffs, checking for patterns, running tests, writing feedback. Repeat for every PR.

Then I set up Claude Code as an automated code review agent. Now it handles the first pass on every PR, and I only step in for architecture decisions. Here's how.

What "AI agent" actually means for code review

An AI coding agent isn't just autocomplete. It's an autonomous system that reads your codebase, understands context, makes decisions, and takes actions across multiple steps.

For code review specifically, that means:

Read the PR diff
Understand how changes relate to the existing architecture
Check for security issues, dependency vulnerabilities, exposed secrets
Verify code follows project conventions
Run the test suite against the PR branch
Generate specific, actionable feedback

That's not a chatbot. That's a coding agent doing real work.

My automated review pipeline

Here's my actual setup using Claude Code:

Step 1: The CLAUDE.md file

Your AI agent needs context. I use a CLAUDE.md file in every project root that tells Claude Code the conventions, file structure, and constraints:

## Code Review Standards
- All functions need explicit return types
- No `any` type — define proper types
- API routes must validate input with Zod
- Database queries go through the repository pattern
- No console.log in production code

This is the rulebook the agent checks every PR against.

Step 2: Custom review skill

I built a reusable review skill that Claude Code follows every time:

# Code Review Skill

## Procedure
1. Read the full diff
2. Check each changed file against CLAUDE.md conventions
3. Run `pnpm typecheck` and `pnpm lint`
4. Look for: missing error handling, security issues, performance problems
5. Generate review with specific line references
6. Categorize issues: blocking vs. suggestion vs. nitpick

Step 3: Hooks for enforcement

Hooks are the hard constraints. They run automatically — Claude Code can't skip them:

{
  "hooks": {
    "PreCommit": [
      {
        "command": "pnpm lint && pnpm typecheck",
        "description": "Lint and type-check before committing"
      }
    ]
  }
}

If the lint fails, the commit doesn't happen. No exceptions.

What the agent catches that I miss

After running this for a month, patterns emerged:

Consistency issues — I'd miss that a new utility function didn't follow the naming pattern from existing ones. The agent catches every instance because it reads the full codebase, not just the diff.

Missing error handling — especially in async code. The agent flags every unhandled promise and missing try-catch in API routes.

Type safety gaps — implicit any types, missing return types, loose union types that should be discriminated. I'd skim past these in manual review.

Security basics — hardcoded strings that look like secrets, SQL injection potential, missing input validation. Tedious to check manually, trivial for the agent.

What it doesn't replace

The agent handles the mechanical review — conventions, patterns, type safety, basic security. I still review:

Architecture decisions — should this be a new service or extend an existing one?
Business logic correctness — does this actually solve the problem?
UX implications — will this change confuse users?
Performance at scale — will this query hold up with 10x the data?

The split is clean: agent handles the "is this code correct?" layer, I handle the "is this the right approach?" layer.

Time saved

Before: 2-3 hours daily on code review
After: 30-45 minutes (mostly architecture-level review)

That's roughly 10 hours per week freed up. And the review quality is more consistent — the agent doesn't get tired at 4pm and start rubber-stamping PRs.

Getting started

You don't need a complex setup. The minimum viable agent review pipeline:

Create a CLAUDE.md with your conventions
Ask Claude Code to review a diff: "Review the changes in this PR for issues"
Iterate on the CLAUDE.md based on what the agent misses or over-flags

That's it. No infrastructure, no CI integration, no setup beyond what you already have.

For the full stack — persistent memory so the agent remembers past review patterns, custom skills for repeatable procedures, and hooks for hard enforcement — the complete setup guide walks through everything.

If you want all of this pre-configured, Claudify ships with code review skills, quality hooks, and 1,727 other skills ready to go.

Are you using AI for code review? What's your setup? Comments below.

MCP Servers Changed How I Use Claude Code — Here's My Setup

Claudify — Tue, 17 Mar 2026 00:13:47 +0000

Claude Code out of the box reads files and runs terminal commands. That's useful, but it's a fraction of what it can do.

MCP (Model Context Protocol) servers extend Claude Code with direct access to databases, GitHub, browsers, Google Sheets, and dozens of other services. Once I set them up, my workflow changed completely — no more copy-pasting data between tools.

Here's how I configured my MCP stack and which servers are actually worth using.

What MCP servers do

Think of them as plugins. Each server exposes tools that Claude Code can call during any task. You don't need to tell it "use the GitHub MCP server" — you just say "create a PR for these changes" and it figures out which tool to call.

The architecture:

You configure a server (locally or remotely)
Claude Code discovers its tools at startup
During tasks, it calls tools as needed
Results flow back into context

It feels native. That's the key insight.

Setting up your first server

One command:

claude mcp add github --scope project -- npx @modelcontextprotocol/server-github

The --scope flag matters:

user — available in all projects
project — only in the current project

Or edit .mcp.json directly:

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_..."
      }
    }
  }
}

The servers I actually use daily

GitHub

Manage issues, PRs, code search without leaving Claude Code. "Find all open PRs that touch the auth module" just works.

Playwright (browser automation)

Claude Code controls a browser — navigates pages, clicks elements, fills forms, takes screenshots. I use this for automated testing and visual verification after deploys.

Database (Turso/SQLite)

Query your database directly. Instead of writing SQL in a separate client and pasting results, Claude Code runs queries and acts on the data inline.

Memory server

A structured knowledge graph that persists across sessions. Facts survive between conversations. This pairs well with CLAUDE.md configuration for project-level context.

My production `.mcp.json`

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["@modelcontextprotocol/server-github"],
      "env": { "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_..." }
    },
    "playwright": {
      "command": "npx",
      "args": ["@anthropic-ai/mcp-server-playwright"]
    },
    "memory": {
      "command": "npx",
      "args": ["@anthropic-ai/mcp-server-memory"]
    }
  }
}

Start with 2-3 servers. Each one adds tool definitions to Claude Code's context window, so more servers = more context consumption. Add what you need, disable what you don't.

The context window trap

This is the thing nobody warns you about. Every enabled MCP server adds tool definitions to Claude Code's system prompt. Ten servers with dozens of tools each means significant context overhead on every request.

This directly impacts rate limits. More context per request = fewer requests before you hit limits.

To manage it:

Disable unused servers: claude mcp disable server-name
Use project-scope for project-specific servers
Use user-scope only for universally useful ones

Before and after MCP

Without MCP	With MCP
Query DB separately → copy → paste into chat	"Find users who signed up this week" — auto-queries
Open GitHub → create PR → copy URL → tell AI	"Create a PR for these changes" — handles everything
Write script → run → read output → paste back	Tool calls happen inline, automatically

The productivity difference is real. MCP eliminates the copy-paste-context-switch loop.

The full picture

MCP servers are one layer of the Claude Code configuration stack. The full stack is:

CLAUDE.md — project context and conventions
Persistent memory — cross-session continuity
Custom skills — repeatable procedures
Hooks — automated guardrails
MCP servers — external tool access

Each layer compounds. I wrote a complete MCP servers guide covering every server worth using and how to build a production stack.

If you want the full configuration stack pre-built — skills, memory, hooks, agents, and MCP setup guides — Claudify packages it all into one install.

What MCP servers are you using? Drop your setup in the comments.

Stop Hitting Claude Code Rate Limits: The Fix Nobody Talks About

Claudify — Tue, 17 Mar 2026 00:11:14 +0000

If you've used Claude Code for more than a week, you've probably hit this:

Rate limit reached. Please wait before retrying.

Frustrating. Especially when your usage dashboard says you're at 30%. Here's what's actually happening and how to fix it.

Why Claude Code burns tokens faster than you think

Claude Code doesn't send one prompt to the API. Each interaction includes your entire conversation history — system prompt, accumulated context, and every file that's been pulled in.

A developer who starts a session and runs 15 iterative commands might be sending 200,000+ input tokens on that final request. The context snowballs.

This is why Claude Code users hit rate limits that chat users at the same subscription tier never encounter.

The immediate fixes

1. Switch models mid-session

/model sonnet

Drops you from Opus to Sonnet. Less capable, but uses fewer resources. For simple tasks, you won't notice the difference.

2. Compact your context

/compact

This compresses your conversation history, dramatically reducing token consumption on subsequent requests. Run it after completing each major task.

3. Start fresh sessions

/clear

Nuclear option — starts a clean session. Best used at natural task boundaries rather than mid-task.

4. Credential reset for stuck sessions

Some users report getting permanently stuck on rate limit errors even after the window resets:

claude logout
# delete cached credentials
claude login

Forces a fresh session state.

The real fix: token efficiency

The developers who rarely hit limits aren't using Claude Code less — they're using it more efficiently.

Be specific in prompts. Vague prompts ("fix the auth") make Claude explore your whole codebase. Specific prompts ("fix the null check in src/auth/validate.ts line 42") hit the target directly.

Batch requests. Instead of five separate "fix this typo" messages, batch them: "Fix all typos in src/components/."

Use CLAUDE.md. A well-written CLAUDE.md configuration file gives Claude your project context upfront. Less back-and-forth = fewer tokens.

Set up persistent memory. A memory system means Claude doesn't waste tokens re-learning your project every session.

The configuration advantage

Here's the pattern: developers who configure Claude Code properly almost never hit rate limits. The configuration layer — CLAUDE.md, memory, skills — reduces the tokens needed per task because Claude already knows your context.

If you want the full configuration stack without building it yourself, Claudify ships with 1,700+ skills, persistent memory, and context management built in. One install and your token efficiency improves immediately.

I wrote a complete rate limit fix guide with more details on pricing tiers, known bugs, and prevention strategies.

What's your rate limit workaround? Share in the comments.

Claude Code vs Cursor: What I Learned Using Both for 30 Days

Claudify — Tue, 17 Mar 2026 00:07:20 +0000

I spent the last month using both Claude Code and Cursor as my primary AI coding tools. Not as a benchmark — as my actual daily workflow. Here's what I found.

The fundamental difference

Cursor is an accelerator. It makes you faster at what you already know how to do. Great autocomplete, visual diffs, smooth editor integration.

Claude Code is a delegator. You describe a task, it goes and does it. Read the codebase, plan the approach, edit files, run tests, fix failures, commit. You come back to working code.

This isn't a small distinction. It changes how you think about your work.

Where Cursor won

Flow-state coding. When I'm writing code and I know exactly what I want, Cursor's autocomplete is faster. Tab-tab-tab, done. No context switch.

Visual refactoring. Selecting a block of code, describing a change, and reviewing a visual diff is genuinely pleasant. For single-file or small-scope changes, Cursor's interactive model feels more natural.

Quick questions. "What does this function do?" — Cursor's inline chat is faster than opening a Claude Code session for throwaway questions.

Where Claude Code won

Complex multi-step tasks. "Add Stripe webhook handling with idempotency keys" — Claude Code reads the existing payment setup, installs dependencies, creates the webhook route, adds signature verification, writes tests, and runs them. One prompt.

Terminal workflows. Git operations, Docker, CI/CD, deployment scripts. Claude Code chains tools natively because it lives in the terminal.

Getting it right the first time. I tracked code rework across both tools. Claude Code required significantly less back-and-forth to produce production-ready code. It reads more context upfront and makes fewer assumptions.

Customization that compounds. This is the big one. Claude Code supports custom skills, persistent memory, and hooks. After a few weeks of configuration, it understood my project's conventions, remembered my architectural decisions, and followed my deployment process without being told.

Cursor doesn't have an equivalent configuration layer. Every session is roughly the same.

My current setup

I use both:

Cursor for flow-state editing, quick refactors, visual diffs
Claude Code for complex tasks, deployments, multi-file changes, anything I want to delegate

The cost of both ($40/month) saves me hours per week. Not a hard ROI to justify.

The configuration gap

The biggest insight: raw Claude Code and configured Claude Code are almost different products. Out of the box, it's good. With a proper CLAUDE.md, persistent memory, and custom skills, it's transformative.

If you want the full configuration stack without building it yourself, check out Claudify — it's a pre-built operating system for Claude Code with 1,700+ skills and persistent memory that installs in one command.

For a deeper comparison with benchmarks and pricing, I wrote a more detailed breakdown on our blog.

Using both tools? What's your split look like?

How I Set Up Claude Code to Run My Entire Dev Workflow

Claudify — Tue, 17 Mar 2026 00:04:33 +0000

I've been using Claude Code as my primary development tool for the past few months. Not as a fancy autocomplete — as an actual workflow engine that handles deployments, testing, code review, and multi-step tasks autonomously.

Here's the setup that made it click.

The problem with default Claude Code

Out of the box, Claude Code is a capable terminal assistant. But it starts fresh every session. It doesn't know your project conventions. It doesn't remember that you prefer named exports, or that your deploy script needs a specific flag, or that the auth module has a known race condition.

Every session, you're re-explaining context. That's the gap.

The configuration stack that changed everything

1. CLAUDE.md — project context in one file

This is the single highest-leverage configuration. Create a CLAUDE.md in your project root with your tech stack, conventions, file structure, and build commands. Claude reads it automatically at session start.

# Project: My App

## Stack
- Next.js 14 (App Router), TypeScript strict, Tailwind
- Drizzle ORM with Postgres, deployed on Vercel

## Commands
- pnpm dev / pnpm build / pnpm test

## Conventions
- Named exports only
- Repository pattern for DB queries
- Never commit .env files

10 minutes to write. Immediately makes every session more productive.

2. Persistent memory

Create .claude/memory.md to maintain context across sessions. Track what you're working on, decisions already made, and known issues. Keep it under 100 lines — memory should be compact, not a changelog.

3. Custom skills

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3. Custom skills

Skills are reusable procedures in .claude/skills/ that Claude follows for repeatable tasks. My most-used skills:

Deploy — runs pre-checks, builds, deploys, verifies
DB migration — generates migration, tests on staging, applies to prod
PR review — checks security, runs tests, reviews against team standards

4. Hooks for guardrails

Hooks in .claude/settings.json enforce rules automatically. My essentials:

Pre-commit: lint + typecheck before every commit
Pre-write: block files containing hardcoded secrets
Post-tool: log every file modification for audit

The result

With this setup, I can say "add Stripe webhook handling" and walk away. Claude Code reads my codebase, understands the architecture (from CLAUDE.md), follows my conventions (from memory), uses the right deploy process (from skills), and can't commit bad code (hooks).

It's not magic — it's configuration compounding over time.

Want the shortcut?

Building all this from scratch takes days. I found Claudify — a pre-built operating system for Claude Code with 1,700+ skills, persistent memory, and automated quality gates. It installs in one command and gives you the full configuration stack immediately.

Whether you build your own setup or use something pre-built, the key insight is the same: Claude Code's value isn't in the model — it's in the configuration layer around it.

What's your Claude Code setup look like? Drop your CLAUDE.md tips in the comments.

Show DEV: I Built an Operating System for Claude Code

Claudify — Mon, 16 Mar 2026 15:35:49 +0000

I've been using Claude Code daily since it launched, and I kept running into the same problems: it forgets everything between sessions, makes the same mistakes twice, and has no structure for complex workflows.

So I built Claudify — a downloadable toolkit that turns Claude Code into a structured operating system.

What It Does

Claudify installs into your project directory and gives Claude Code:

1,727 expert skills across 31 categories (SEO, debugging, deployment, testing, etc.)
9 specialist agents with persistent memory that survives between sessions
21 slash commands for common workflows (/commit, /review-pr, /audit, etc.)
9 automated quality checks via pre/post hooks that catch errors before they ship
A self-improving knowledge base that learns from corrections and gets smarter over time

The Problem I Was Solving

Out of the box, Claude Code is powerful but stateless. Every session starts from zero. It doesn't know your project conventions, your preferred patterns, or what went wrong last time.

I wanted a system where Claude Code could:

Remember project context, coding patterns, and past decisions
Follow procedures consistently instead of improvising every time
Catch its own mistakes through automated hooks and quality gates
Route tasks to specialist agents (content, data, debugging) with the right domain knowledge

How It Works

One command installs everything:

npx claudify init

This drops a .claude/ directory into your project with:

CLAUDE.md — project instructions Claude reads automatically
agents/ — specialist subagents with their own memory files
skills/ — domain knowledge loaded on demand
commands/ — slash command definitions
settings.json — hook configurations for quality gates
memory.md — persistent context that survives between sessions

Claude Code reads CLAUDE.md on startup, which bootstraps the entire system. No IDE plugins, no cloud dependencies, no subscriptions.

What Makes It Different

Most AI coding tools focus on autocomplete or chat. Claudify focuses on operational structure — making Claude Code reliable enough to handle real workflows autonomously.

The key insight: Claude Code doesn't need more intelligence. It needs better memory, clearer procedures, and guardrails that prevent drift.

Tech Stack

Works with Claude Code, Cursor, Windsurf, and any tool that reads CLAUDE.md
Pure file-based — no servers, no APIs, no vendor lock-in
Skills are markdown files with frontmatter metadata
Hooks are shell scripts triggered by Claude Code events
Agents are markdown definitions with persistent memory files

Try It

The project is at claudify.tech. One-time purchase ($49 full / $19 skills-only pack), no subscription.

Happy to answer questions about the architecture, how the memory system works, or how the agent routing is structured. Would love feedback from other Claude Code users on what workflows you'd want automated.

Built with Claude Code, of course.

DEV Community: Claudify

The CLAUDE.md mistakes I kept making (and what fixed them)

Mistake 1: Repeating instructions in chat instead of writing them down

Mistake 2: Believing CLAUDE.md instructions are enforcement

Mistake 3: Letting it read whole files when a slice would do

Mistake 4: One giant session for everything

Mistake 5: Compacting mid-task

Mistake 6: Leaving every MCP server switched on

Mistake 7: Exploring in the main thread

The pattern underneath all of these

I Replaced My Entire Code Review Process with an AI Agent

What "AI agent" actually means for code review

My automated review pipeline

Step 1: The CLAUDE.md file

Step 2: Custom review skill

Step 3: Hooks for enforcement

What the agent catches that I miss

What it doesn't replace

Time saved

Getting started

MCP Servers Changed How I Use Claude Code — Here's My Setup

What MCP servers do

Setting up your first server

The servers I actually use daily

GitHub

Playwright (browser automation)

Database (Turso/SQLite)

Memory server

My production .mcp.json

The context window trap

Before and after MCP

The full picture

Stop Hitting Claude Code Rate Limits: The Fix Nobody Talks About

Why Claude Code burns tokens faster than you think

The immediate fixes

1. Switch models mid-session

2. Compact your context

3. Start fresh sessions

4. Credential reset for stuck sessions

The real fix: token efficiency

The configuration advantage

Claude Code vs Cursor: What I Learned Using Both for 30 Days

The fundamental difference

Where Cursor won

Where Claude Code won

My current setup

The configuration gap

How I Set Up Claude Code to Run My Entire Dev Workflow

The problem with default Claude Code

The configuration stack that changed everything

1. CLAUDE.md — project context in one file

2. Persistent memory

3. Custom skills

#T h4e. pHrooobklse mf owri tghu adredfraauillts

3. Custom skills

4. Hooks for guardrails

The result

Want the shortcut?

Show DEV: I Built an Operating System for Claude Code

What It Does

The Problem I Was Solving

How It Works

What Makes It Different

Tech Stack

Try It

My production `.mcp.json`