/replay-learnings

name: replay-learnings
description: 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".

Replay Learnings

Like muscle memory for your coding sessions. Find and surface relevant learnings before you start working.

Trigger

Use when starting a new task, saying "what do I know about", "before I start", "replay", or "remind me about".

Workflow

1. Extract keywords from the task description (e.g. "auth refactor" → `auth`, `middleware`, `refactor`). 2. Search learnings/memory for matching patterns:

   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"

3. Check session history for similar work — what was the correction rate? 4. Surface the top learnings ranked by relevance. 5. If no learnings found, suggest starting with the scout agent to explore first.

Output

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)

Guardrails

• Rank by relevance, not recency.
• Include the original mistake context so the learning is actionable.
• Flag high correction-rate sessions as areas requiring extra care.
• If no learnings match, say so explicitly rather than forcing irrelevant results.