Show session analytics, learning patterns, correction trends, heatmaps, and productivity metrics. Computes stats from project memory and session history. Use when asking for stats, statistics, progress, how am I doing, coding history, or dashboard.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Surface patterns from learnings and session history.
Use when asking "show stats", "how am I doing", "analytics", "insights", "heatmap", or "correction rate".
Gather data from these locations before computing metrics:
# Session history and learnings
cat .claude/LEARNED.md 2>/dev/null || cat CLAUDE.md | grep -A999 "LEARNED"
cat .claude/learning-log.md 2>/dev/null
# Session activity
git log --oneline --since="today" --author="$(git config user.name)"
git diff --stat
A correction is any instance where the user redirected, fixed, or overrode agent output during a session. Count [LEARN] entries and explicit correction markers in session history.
Session Insights
Duration: 47 min
Edits: 23 files modified
Corrections: 2 self-corrections applied
Learnings: 3 new patterns captured
Context: 62% used (safe)
Learning Insights (42 total)
Top categories:
Testing 12 learnings (29%)
Navigation 8 learnings (19%)
Git 7 learnings (17%)
Quality 6 learnings (14%)
Most applied:
#12 [Testing] Run tests before commit — 15 times
#8 [Navigation] Confirm path for common names — 11 times
Stale learnings (never applied):
#15 [Editing] Prefer named exports — 0 times (45 days old)
Correction Heatmap
By category (all time):
████████████ Testing 34 corrections
████████ Navigation 22 corrections
██████ Git 18 corrections
████ Quality 12 corrections
Hot learnings (most corrected, least learned):
- [Testing] Mock external deps — corrected 8x, learned 0x
→ Consider: /learn-rule to capture this permanently
Cold learnings (learned but never applied):
- [Editing] Use named exports — learned 45 days ago, applied 0x
→ Consider removing if no longer relevant
Productivity (last 10 sessions)
Avg session: 35 min
Avg edits/session: 18
Correction rate: 12% (improving)
Learning capture: 2.1 per session
Formatted analytics report with:
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.