Retrospect Protocol
Mine the current session for learnings and translate them into durable memories. Sessions generate massive amounts of information — your job is to find the fraction worth remembering, structure it properly, and persist it.
Mode Selection
| Signal | Mode | Phases | When |
|---|---|---|---|
| Casual question ("뭘 배웠지?", "learnings") | Quick | 1a → 3 → 6 | Simple summary, end-of-session |
| Maintenance request ("메모리 정리", "cleanup") | Full | 1~6 all | Memory audit, bulk operations |
| Track argument ("/retrospect OR") | Full | 1~6 all | Deep track analysis |
| During retrospective (③ step) | Full | 1~6 all | Post-audit protocol |
Mode is selected automatically — do not ask the user.
Execution Context
| Context | Detection | Approval Behavior |
|---|---|---|
| Interactive | Main session, user is responding | Present candidates → wait for approval → execute |
| Headless | Subagent, no human to respond | Auto-approve threshold-met candidates → defer ambiguous → execute → report |
In headless mode, NEVER ask questions or wait for input. Any prompt will block indefinitely. Auto-approve what meets threshold rules, defer the rest as ⏭️ deferred to orchestrator.
Setup
Read config: ${CLAUDE_PLUGIN_ROOT}/config.json → plugin.locale
Locate memory: use Glob to find **/memory/MEMORY.md. Read it to understand existing memories (avoid duplicates, check line count < 200).
Phases
Phase 1: Gather Sources
Read references/gathering.md for detailed source collection methods.
Quick mode: conversation + git log only (skip audit_history). Full mode: conversation + audit_history + git log.
Phase 2: Deduplicate (Full mode only)
Read existing memory files → build topic index → mark candidates as "new" or "update".
Phase 3: Generate Candidates
Read references/candidates.md for memory format template, categories, and threshold rules.
4 categories: feedback (corrections + confirmations), project (work context), user (role/preferences), reference (external pointers).
Phase 4-6: Present → Execute → Verify
Read references/execution.md for presentation format, approval flow, and integrity checks.
- Interactive: present table → wait for approval
- Headless: auto-approve → execute → report with deferred items
Integration with Retrospective
When invoked during post-audit retrospective (③ Memory cleanup step):
- This skill handles memory extraction and cleanup
- Other retro steps (① what went well, ② problems, ④ feedback, ⑤ Act) continue independently
- Learnings needing code changes → work-catalog items (not memories)
Memory Authority
Only this skill and agents may read/write memories. Other skills (verify, merge, planner) must not access the memory directory directly. This prevents memory pollution from non-learning contexts.
| Actor | Memory Access |
|---|---|
consensus-loop:retrospect | Read + Write (primary owner) |
| Agents (orchestrator, implementer) | Read only (via auto-memory system) |
| Other skills (verify, merge, planner) | No access — request via orchestrator if needed |
Rules
- Data before candidates — gather all sources before generating
- Deduplicate before presenting — check existing memories first (Full mode)
- Every candidate cites a source — no unsupported claims
- User approves everything (interactive) / auto-approve with thresholds (headless)
- Why + How to apply required for feedback/project types
- Prefer update over create — one good memory > two overlapping
- Show what you skipped — user may disagree with threshold
- Verify after execution — MEMORY.md integrity check (Full mode)
- Never block in headless mode — no questions, no prompts, no "should I?"
- Memory authority — only this skill writes memories; other skills must not touch memory files