post-mortem

name	post-mortem
description	Review completed work and learn.
practices	["dora-metrics","sre","lean-startup"]
hexagonal_role	domain
consumes	["implement","vibe","council"]
produces	["result.json"]
context_rel	[{"kind":"shared-kernel","with":"standards"}]
skill_api_version	1
metadata	{"tier":"judgment","dependencies":["council","beads"]}
context	{"window":"fork","intent":{"mode":"task"},"sections":{"exclude":["HISTORY"]},"intel_scope":"full"}
output_contract	skills/council/schemas/verdict.json

Post-Mortem Skill

Purpose: Wrap up completed work — validate it shipped correctly, extract learnings, process the knowledge backlog, activate high-value insights, and retire stale knowledge.

Runtime note: Hook-driven closeout is runtime-dependent. Claude/OpenCode can wire Phase 2-5 maintenance through lifecycle hooks. Codex CLI v0.115.0+ supports native hooks (same behavior). For older Codex versions without hook surfaces, finish closeout with ao codex stop.

Loop position

Move 7 (capture evidence + learning, then ratchet) of the operating loop. Two outputs per loop turn: evidence (test names, snapshot keys, council verdicts, citation events) recorded against the bead and .agents/ratchet/; learnings promoted only under the ratchet rules — noticed once stays in the handoff, repeats twice goes to .agents/learnings/, changes future behavior updates a SKILL.md or template, must-never-regress becomes a gate, core doctrine promotes into PRODUCT.md/GOALS.md/docs/cdlc.md. Most observations die at handoff. That is correct.

Six phases:

1. Council — Did we implement it correctly?
2. Extract — What did we learn?
3. Process Backlog — Score, deduplicate, and flag stale learnings
4. Activate — Promote high-value learnings to MEMORY.md and constraints
5. Retire — Archive stale and superseded learnings
6. Harvest — Surface next work for the flywheel

---

Quick Start

/post-mortem                    # wraps up recent work
/post-mortem epic-123           # wraps up specific epic
/post-mortem --quick "insight"  # quick-capture single learning (no council)
/post-mortem --scope=pr <num>   # learn from a merged/rejected PR outcome (absorbed /pr-retro)
/post-mortem --process-only     # skip council+extraction, run Phase 3-5 on backlog
/post-mortem --skip-activate    # extract + process but don't write MEMORY.md
/post-mortem --deep recent      # thorough council review
/post-mortem --mixed epic-123   # cross-vendor (Claude + Codex)
/post-mortem --skip-checkpoint-policy epic-123  # skip ratchet chain validation

Codex Closeout

Codex CLI v0.115.0+ has native hooks and handles closeout automatically (no extra steps needed). For older Codex versions (hookless fallback), run these after the post-mortem workflow writes learnings and next work:

ao codex stop
ao codex status

ao codex stop uses the latest transcript or history fallback to queue/persist learnings and run close-loop maintenance without runtime hooks.

---

Flags

Flag	Default	Description
--quick "text"	off	Quick-capture a single learning directly to .agents/learnings/ without running a full post-mortem. Formerly handled by /retro --quick.
--scope=pr [num]	off	Read a merged/rejected PR outcome as the wrap-up signal instead of a closed bead/epic — mine reviewer feedback into a PR learning. Absorbed the former pr-retro skill. See references/pr-scope.md.
--process-only	off	Skip council and extraction (Phase 1-2). Run Phase 3-5 on the existing backlog only.
--skip-activate	off	Extract and process learnings but do not write to MEMORY.md (skip Phase 4 promotions).
--deep	off	3 judges (default for post-mortem)
--mixed	off	Cross-vendor (Claude + Codex) judges
--explorers=N	off	Each judge spawns N explorers before judging
--debate	off	Two-round adversarial review
--skip-checkpoint-policy	off	Skip ratchet chain validation
--skip-sweep	off	Skip pre-council deep audit sweep

---

Quick Mode

Read references/quick-mode.md when you need the --quick flag procedure (slug generation, direct learning write, confirmation).

PR-Outcome Scope (--scope=pr)

Read references/pr-scope.md when invoked with --scope=pr. It swaps the bead/epic wrap-up signal for a PR's merge/reject/changes-requested outcome: discover the PR, analyze the outcome, mine reviewer feedback via gh, extract success/failure patterns, and write a dated PR learning to .agents/learnings/. After the PR learning lands, the standard maintenance phases (process → activate → retire → harvest) run as usual. The trigger phrases "PR retro", "learn from this PR", and the legacy pr-retro command all route here.

---

Execution Steps

Read references/execution-steps.md when you need the full Phase 1 procedure: pre-flight checks, reference loading (Step 0.4), checkpoint-policy preflight (0.5), plan/spec loading (Steps 1-2.3), closure integrity audit (2.4), metadata verification (2.5), deep audit sweep (2.6), council invocation (Step 3), and prediction accuracy (3.5).

Step 2.1: Load Compiled Prevention Context

Before council and retro synthesis, load compiled prevention outputs when they exist:

• .agents/planning-rules/*.md
• .agents/pre-mortem-checks/*.md

Use these compiled artifacts first, then fall back to .agents/findings/registry.jsonl only when compiled outputs are missing or incomplete. Carry matched finding IDs into the retro as Applied findings / Known risks applied context so post-mortem can judge whether the flywheel actually prevented rediscovery.

Phase 2: Extract Learnings

Read references/phase-2-extract.md when you need the inline learning extraction procedure: gather context (EX.1), classify (EX.2), write learnings (EX.3), test pyramid gap analysis (EX.3.5), scope classification (EX.4), findings registry (EX.5-6).

Before backlog processing, normalize reusable council findings into .agents/findings/registry.jsonl.

Use the tracked contract in docs/contracts/finding-registry.md:

• persist only reusable findings that should change future planning or review behavior
• require dedup_key, provenance, pattern, detection_question, checklist_item, applicable_when, and confidence
• applicable_when must use the controlled vocabulary from the contract
• append or merge by dedup_key
• use the contract's temp-file-plus-rename atomic write rule

After the registry mutation, refresh compiled outputs immediately so the same session can benefit from the updated prevention set. If hooks/finding-compiler.sh exists, run:

bash hooks/finding-compiler.sh --quiet 2>/dev/null || true

Step ACT.3: Feed Next-Work

Actionable improvements identified during processing -> append one schema v1.4 batch entry to .agents/rpi/next-work.jsonl using the tracked contract in ../../docs/contracts/next-work.schema.md and the write procedure in references/harvest-next-work.md. Follow the claim/finalize lifecycle documented in references/harvest-next-work.md.

The machine-checkable typed contract is the committed JSON Schema pair ../../schemas/next-work-batch.v1.schema.json (one JSONL line = one batch entry) and ../../schemas/next-work-item.v1.schema.json (each items[] element). Validate written rows with ../../scripts/validate-next-work.sh (--strict to reject malformed output naming the offending field; advisory by default). The inline bash in references/harvest-next-work.md remains a dependency-free fallback when the script is unavailable.

mkdir -p .agents/rpi
# Build VALID_ITEMS via the schema-validation flow in references/harvest-next-work.md
# Then append one entry per post-mortem / epic.
# If a harvested item already maps to a known proof surface, preserve it on the
# item as "proof_ref" instead of burying target IDs in free text. Example item:
# [{"title":"Verify the parity gate after proof propagation lands","type":"task","severity":"medium","source":"council-finding","description":"Re-run the targeted validator after the follow-up lands.","target_repo":"agentops","proof_ref":{"kind":"execution_packet","run_id":"6f36a5640805","path":".agents/rpi/runs/6f36a5640805/execution-packet.json"}}]
ENTRY_TIMESTAMP="$(date -Iseconds)"
SOURCE_EPIC="${EPIC_ID:-recent}"
VALID_ITEMS_JSON="${VALID_ITEMS_JSON:-[]}"

printf '%s\n' "$(jq -cn \
  --arg source_epic "$SOURCE_EPIC" \
  --arg timestamp "$ENTRY_TIMESTAMP" \
  --argjson items "$VALID_ITEMS_JSON" \
  '{
    source_epic: $source_epic,
    timestamp: $timestamp,
    items: $items,
    consumed: false,
    claim_status: "available",
    claimed_by: null,
    claimed_at: null,
    consumed_by: null,
    consumed_at: null
  }'
)" >> .agents/rpi/next-work.jsonl

# Validate the written contract (advisory; add --strict to gate).
bash scripts/validate-next-work.sh .agents/rpi/next-work.jsonl

Step ACT.4: Update Marker

date -Iseconds > .agents/ao/last-processed

This must be the LAST action in Phase 4.

Phases 3-6 (Maintenance): Read references/maintenance-phases.md for backlog processing, activation, retirement, and harvesting phases. Load when --process-only flag is set or when running full post-mortem.

Reporting and Workflow

Read references/user-reporting.md when you need the Step 7 report template, mandatory next-/rpi suggestion format, workflow integration diagram, and example invocations.

Examples

Read references/user-reporting.md for full example invocations and what happens in each mode.

Troubleshooting

Problem	Cause	Solution
Council times out	Epic too large or too many files changed	Split post-mortem into smaller reviews or increase timeout
No next-work items harvested	Council found no tech debt or improvements	Flywheel stable — write entry with empty items array to next-work.jsonl
Checkpoint-policy preflight blocks	Prior FAIL verdict in ratchet chain without fix	Resolve prior failure (fix + re-vibe) or skip checkpoint-policy via --skip-checkpoint-policy
Metadata verification fails	Plan vs actual files mismatch or missing cross-references	Include failures in council packet as context.metadata_failures — judges assess severity

---

Compound-Engineering Retro (--compound)

A comparative-delta mode for projects that run ao goals measure repeatedly across iterations of the same domain slice. Use when a slice has ≥2 iterations in the verdict ledger and you want to know: what improved, what regressed, and what the learning yield was since the last run.

Trigger: run this mode after any ao goals measure where the slice has a prior iteration record in .agents/goals/verdict-ledger.json.

# Confirm ≥2 iterations exist for a directive in the slice:
jq '[.records[] | select(.record_type=="iteration" and .directive_id=="d-<id>")] | length' \
   .agents/goals/verdict-ledger.json

# Run a new iteration (appends one record per directive):
ao goals measure

# Browse iteration history:
ao goals history --goal <directive-id>

Then follow the step-by-step procedure in references/compound-engineering-retro.md (Steps CE.0–CE.5): extract N and N-1 records from the ledger, compute the verdict and satisfaction delta, count learning yield, and write the delta as a draft learning to .agents/learnings/YYYY-MM-DD-<slice>-iter-delta.md.

The output learning carries status: draft and the run IDs of both iterations; human or Tier-3 synthesis promotes it to status: reviewed.

Closing the loop with re-steer. When the delta shows a directive failing chronically, the verdict ledger also drives auto re-steer: ao goals steer recommend prints policy-driven directive mutations from the same ledger, and ao goals steer apply writes the chosen mutation to GOALS.md — human-gated, via the non-lossy patcher (policy auto_apply plus explicit confirmation; ADR-0006). The compound retro names what regressed; re-steer proposes how the directive should change. See the /goals skill.

---

See Also

• skills/council/SKILL.md — Multi-model validation council
• skills/vibe/SKILL.md — Council validates code (/vibe after coding)
• skills/pre-mortem/SKILL.md — Council validates plans (before implementation)

Reference Documents

• references/post-mortem.feature — Executable spec: validate-shipped, ratcheted learning promotion, next-work harvest, result.json (soc-qk4b.2)
• references/pr-retro.feature — Executable spec (--scope=pr): categorize PR feedback, extract success/failure patterns by outcome, write a dated PR learning (soc-qk4b)
• references/pr-scope.md — --scope=pr: PR discovery, outcome analysis, gh feedback mining, PR learning template (absorbed /pr-retro)
• references/harvest-next-work.md
• references/learning-templates.md
• references/plan-compliance-checklist.md
• references/closure-integrity-audit.md
• references/security-patterns.md
• references/checkpoint-policy.md
• references/metadata-verification.md
• references/context-gathering.md
• references/output-templates.md
• references/backlog-processing.md
• references/activation-policy.md
• references/prediction-tracking.md
• references/retro-history.md
• references/streak-tracking.md
• references/maintenance-phases.md
• references/four-surface-closure.md
• references/quick-mode.md
• references/execution-steps.md
• references/phase-2-extract.md
• references/user-reporting.md
• references/compound-engineering-retro.md