Surface past learnings relevant to the current task before starting work. Searches correction history, recalls past mistakes, and applies prior patterns. Use when starting a task, saying "what do I know about", "previous mistakes", "lessons learned", or "remind me about".
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Like muscle memory for your coding sessions. Find and surface relevant learnings before you start working.
Use when starting a new task, saying "what do I know about", "before I start", "replay", or "remind me about".
auth, middleware, refactor).grep -i "auth\|middleware" .claude/LEARNED.md 2>/dev/null
grep -i "auth\|middleware" .claude/learning-log.md 2>/dev/null
grep -A2 "\[LEARN\]" CLAUDE.md | grep -i "auth\|middleware"
REPLAY BRIEFING: <task>
=======================
Past learnings (ranked by relevance):
1. [Testing] Always mock external APIs in auth tests (applied 8x)
Mistake: Called live API in tests, caused flaky failures
2. [Navigation] Auth middleware is in src/middleware/ not src/auth/ (applied 5x)
3. [Quality] Add error boundary around auth state changes (applied 3x)
Session history for similar work:
- 2026-02-01: auth refactor — 23 edits, 2 corrections (8.7% rate)
- 2026-01-28: auth middleware — 15 edits, 4 corrections (26.7% rate)
^ Higher correction rate — review patterns before starting
Suggested approach:
- Mock external APIs (learning #1)
- Check src/middleware/ first for auth code (learning #2)
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Coordinate multiple Claude Code sessions as a team — lead + teammates with shared task lists, mailbox messaging, and file-lock claiming. Patterns for team sizing, task decomposition, and when to use teams vs sub-agents vs worktrees.
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Auto-configure quality gates, hooks, and settings for a new project. Detects project type and sets up appropriate tooling. Use when onboarding a new codebase.
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Decompose large-scale changes into independent units and spawn parallel agents in isolated worktrees. Use for migrations, refactors, codemods, and any change touching 10+ files with the same pattern.
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Capture a user-reported defect as a durable GitHub issue written in the project's own domain language. Explores the codebase in parallel for context but never leaks file paths or line numbers into the issue. Use when the user reports a bug conversationally, runs a QA pass, or says "file an issue", "log this as a bug", "capture this".
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Smart context compaction with state preservation. Saves critical files, task progress, and working state before compaction, restores after. Use before manual compact or when auto-compact triggers.
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Master the four operations of context engineering — Write, Select, Compress, Isolate. Manage token budgets, compaction strategies, and context partitioning to keep AI sessions sharp and efficient.
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