Replay learnings

Skill rohitg00/pro-workflow/skills/replay-learnings

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".From its SKILL.md

Install

npx -y skills add rohitg00/pro-workflow --skill replay-learnings

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.

SKILL.md

2.1 KB, 488 tokens by cl100k_base, as published. Nobody here has run it

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

Extract keywords from the task description (e.g. "auth refactor" → auth, middleware, refactor).

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"

Check session history for similar work — what was the correction rate?
Surface the top learnings ranked by relevance.
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.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.