common-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

1. Detect signal — identify which surface triggered this skill:

• Pre-write violation — common-feedback-reporter violation block emitted with Auto-fixed: YES
• User correction — user used correction language mid-session
• Session retrospective — correction loop found during common-session-retrospective

1. Read AGENTS_LEARNING.md — count existing ## Agent Learning Log: Iteration headers → N
2. Append entry — write Iteration #(N+1) using format in Log Entry Format
3. 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

• Log Entry Format — full entry template + AGENTS_LEARNING.md bootstrap

Canonical response anchors

When this skill applies, preserve the following domain terminology or equivalent concrete examples in the answer when relevant:

• Append to AGENTSLEARNING,append

• AGENTS_LEARNING.md

• Iteration

• Additional task-grounded exact anchors: Pre-write; trigger