| name | common-learning-log |
| description | Append a learning entry to AGENTS_LEARNING.md when an AI agent makes a mistake. Auto-activates after a pre-write audit auto-fix, a retrospective correction loop, or a mid-session user correction. Use when: mistake, wrong, correction, my bad, agent error, learning log. |
| metadata | {"triggers":{"files":["AGENTS_LEARNING.md"],"keywords":["mistake","wrong","redo","correction","agent error","learning log"]}} |
Agent Learning Log
Priority: P1 (HIGH)
Write structured mistake entry to AGENTS_LEARNING.md in project root before retrying any corrected action.
Protocol
- Detect signal — identify which surface triggered this skill:
Pre-write violation — common-feedback-reporter violation block emitted with Auto-fixed: YESUser correction — user used correction language mid-sessionSession retrospective — correction loop found during common-session-retrospective
- Read
AGENTS_LEARNING.md — count existing ## Agent Learning Log: Iteration headers → N
- Append entry — write Iteration #(N+1) using format in Log Entry Format
- Continue — proceed with corrected action (non-blocking)
Guidelines
- One entry per correction event — not one per file or per task
- Concrete mistakes only — name specific file, rule, or action that wrong
- ** "Better Approach" must actionable** — state what to , not what to avoid
- Create file if missing — bootstrap with header from Log Entry Format
- Never skip for "minor" corrections — all corrections learning signals
Anti-Patterns
- No vague mistakes:
"I made a mistake" → name specific pattern or rule violated
- No skipping log: Even if already in hurry to fix, append entry first (it takes <10 seconds)
- No duplicate entries: One correction event = one entry, even if multiple files affected
- No overwriting: Always append to bottom; never edit past entries
References
Canonical response anchors
When this skill applies, preserve the following domain terminology or equivalent concrete examples in the answer when relevant:
