retrospective

name	retrospective
description	Analyse recent session transcripts to extract learnings, discover pain points, and persist them into memory/skills/rules. Run manually when the retrospective counter is due (session-end reminds you at 5+).

retrospective

Analyse recent Claude Code sessions to surface patterns, corrections, and pain points that should become rules, memory, or skills.

1. Extract signals

Analyse exactly the sessions that have elapsed since the last retrospective. The cline-hook session counter tracks this - read it and pass it to the script (clamp to a minimum of 1 if the counter is missing or 0, and let an explicit SESSIONS override win):

count=$(cline-hook retro-count --get 2>/dev/null || echo 0)
sessions=${SESSIONS:-$(( count > 0 ? count : 1 ))}
python3 "<base-dir>/extract_signals.py" --sessions "$sessions"

Where <base-dir> is the base directory shown at the top of this skill's context. Save the JSON output for the next steps.

2. Analyse (parallel subagents)

Fan out 3 Agent calls in parallel with the extracted JSON data:

Agent A: Corrections and Preferences

Give it the corrections array. For each correction, determine what rule or preference the user was enforcing. Group similar corrections. For each group:

• If a steering rule or skill should capture this, identify the file and suggest an addition
• If it is a preference, persist to memory (user-preferences/ entity, global scope)
• If it is project-specific, persist to the relevant project scope
• Report back: for each group, state whether it was already captured or newly persisted, and what action was taken

Agent B: Failures and Knowledge Gaps

Give it the retries array. Identify:

• Build/test failures that revealed missing setup or configuration (persist as pattern/ or knowledge/ entities)
• Repeated failures suggesting a missing permission allowlist entry (suggest settings.json changes)
• Patterns where the agent took wrong approaches repeatedly (suggest rule additions)
• Report: knowledge gaps found and how they were addressed

Agent C: Session Health and Workflow

Give it long_sessions and tool_patterns. Identify:

• Why long sessions were long (check titles/projects for context)
• Heavy tool usage that suggests missing shortcuts or automation
• Whether subagent usage was effective or could be improved
• Report: session health summary and workflow suggestions

3. Synthesise and report to user

After all subagents complete, compile a summary and show it to the user. This is the primary output of the skill - the user must see what was found and what actions were taken:

• Number of sessions analysed
• Gaps closed: corrections that were NOT previously captured but are now persisted (most important - show what was learned)
• Already captured: corrections that matched existing rules/memory (brief count)
• Any rule/skill file changes proposed (show the diff or addition)
• Session health observations (long sessions, workflow suggestions)
• Suggested next actions

4. Reset counter

cline-hook retro-count --reset