consensus-loop:retrospect

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

1. Data before candidates — gather all sources before generating
2. Deduplicate before presenting — check existing memories first (Full mode)
3. Every candidate cites a source — no unsupported claims
4. User approves everything (interactive) / auto-approve with thresholds (headless)
5. Why + How to apply required for feedback/project types
6. Prefer update over create — one good memory > two overlapping
7. Show what you skipped — user may disagree with threshold
8. Verify after execution — MEMORY.md integrity check (Full mode)
9. Never block in headless mode — no questions, no prompts, no "should I?"
10. Memory authority — only this skill writes memories; other skills must not touch memory files