post-mortem

boshu2/agentops · updated Apr 8, 2026

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$npx skills add https://github.com/boshu2/agentops --skill post-mortem
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summary

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

skill.md

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 does not expose that hook surface, so Codex sessions should finish closeout with ao codex stop.

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 Retire — Archive stale and superseded learnings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 --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

In Codex hookless mode, 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.
--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

Given /post-mortem --quick "insight text":

Quick Step 1: Generate Slug

Create a slug from the content: first meaningful words, lowercase, hyphens, max 50 chars.

Quick Step 2: Write Learning Directly

Write to: .agents/learnings/YYYY-MM-DD-quick-<slug>.md

---
type: learning
source: post-mortem-quick
date: YYYY-MM-DD
maturity: provisional
utility: 0.5
---

# Learning: <Short Title>

**Category**: <auto-classify: debugging|architecture|process|testing|security>
**Confidence**: medium

## What We Learned

<user's insight text>

## Source

Quick capture via `/post-mortem --quick`

This skips the full pipeline — writes directly to learnings, no council or backlog processing.

Quick Step 3: Confirm

Learned: <one-line summary>
Saved to: .agents/learnings/YYYY-MM-DD-quick-<slug>.md

For deeper reflection, use `/post-mortem` without --quick.

Done. Return immediately after confirmation.


Execution Steps

Pre-Flight Checks

Before proceeding, verify:

  1. Git repo exists: git rev-parse --git-dir 2>/dev/null — if not, error: "Not in a git repository"
  2. Work was done: git log --oneline -1 2>/dev/null — if empty, error: "No commits found. Run /implement first."
  3. Epic context: If epic ID provided, verify it has closed children. If 0 closed children, error: "No completed work to review."

If --process-only: Skip Pre-Flight Checks through Step 3. Jump directly to Phase 3: Process Backlog.

Step 0.4: Load Reference Documents (MANDATORY)

Before Step 0.5 and Step 2.5, load required reference docs into context using the Read tool:

REQUIRED_REFS=(
  "skills/post-mortem/references/checkpoint-policy.md"
  "skills/post-mortem/references/metadata-verification.md"
  "skills/post-mortem/references/closure-integrity-audit.md"
  "skills/post-mortem/references/four-surface-closure.md"
)

For each reference file, use the Read tool to load its content and hold it in context for use in later steps. Do NOT just test file existence with [ -f ] -- actually read the content so it is available when Steps 0.5 and 2.5 need it.

If a reference file does not exist (Read returns an error), log a warning and add it as a checkpoint warning in the council context. Proceed only if the missing reference is intentionally deferred.

Step 0.5: Checkpoint-Policy Preflight (MANDATORY)

Read references/checkpoint-policy.md for the full checkpoint-policy preflight procedure. It validates the ratchet chain, checks artifact availability, and runs idempotency checks. BLOCK on prior FAIL verdicts; WARN on everything else.

Step 1: Identify Completed Work and Record Timing

Record the post-mortem start time for cycle-time tracking:

PM_START=$(date +%s)

If epic/issue ID provided: Use it directly.

If no ID: Find recently completed work:

# Check for closed beads
bd list --status closed --since "7 days ago" 2>/dev/null | head -5

# Or check recent git activity
git log --oneline --since="7 days ago" | head -10

Step 1.5: RPI Session Metrics

Read .agents/rpi/rpi-state.json and extract session ID, phase, verdicts, and streak data. If absent or unparseable, skip silently. Prepend a tweetable summary to the report: > RPI streak: N consecutive days | Sessions: N | Last verdict: PASS/WARN/FAIL. See references/streak-tracking.md for extraction logic and fallback behavior.

Step 2: Load the Original Plan/Spec

Before invoking council, load the original plan for comparison:

  1. If epic/issue ID provided: bd show <id> to get the spec/description
  2. Search for plan doc: ls .agents/plans/ | grep <target-keyword>
  3. Check git log: git log --oneline | head -10 to find the relevant bead reference

If a plan is found, include it in the council packet's context.spec field:

{
  "spec": {
    "source": "bead na-0042",
    "content": "<the original plan/spec text>"
  }
}

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.

Step 2.2: Load Implementation Summary

Check for a crank-generated phase-2 summary:

PHASE2_SUMMARY=$(ls -t .agents/rpi/phase-2-summary-*-crank.md 2>/dev/null | head -1)
if [ -n "$PHASE2_SUMMARY" ]; then
    echo "Phase-2 summary found: $PHASE2_SUMMARY"
    # Read the summary with the Read tool for implementation context
fi

If available, use the phase-2 summary to understand what was implemented, how many waves ran, and which files were modified.

Step 2.3: Reconcile Plan vs Delivered Scope

Compare the original plan scope against what was actually delivered:

  1. Read the plan from .agents/plans/ (most recent)
  2. Compare planned issues against closed issues (bd children <epic-id>)
  3. Note any scope additions, removals, or modifications
  4. Include scope delta in the post-mortem findings

Step 2.4: Closure Integrity Audit (MANDATORY)

Read references/closure-integrity-audit.md for the full procedure. Mechanically verifies:

  1. Evidence precedence per child — every closed child resolves on the strongest available evidence in this order: commit, then staged, then worktree
  2. Phantom bead detection — flags children with generic titles ("task") or empty descriptions
  3. Orphaned children — beads in bd list but not linked to parent in bd show
  4. Multi-wave regression detection — for crank epics, checks if a later wave removed code added by an earlier wave
  5. Stretch goal audit — verifies deferred stretch goals have documented rationale

Include results in the council packet as context.closure_integrity. WARN on 1-2 findings, FAIL on 3+.

If a closure is evidence-only, emit a proof artifact with bash skills/post-mortem/scripts/write-evidence-only-closure.sh and cite at .agents/releases/evidence-only-closures/<target-id>.json. Record evidence_mode plus repo-state detail for replayability. A valid durable packet is acceptable audit evidence even when the child intentionally has no scoped-file section.

Step 2.5: Pre-Council Metadata Verification (MANDATORY)

Read references/metadata-verification.md for the full verification procedure. Mechanically checks: plan vs actual files, file existence in commits, cross-references in docs, and ASCII diagram integrity. Failures are included in the council packet as context.metadata_failures.

Step 2.6: Pre-Council Deep Audit Sweep

Skip if --quick or --skip-sweep.

Before council runs, dispatch a deep audit sweep to systematically discover issues across all changed files. This uses the same protocol as /vibe --deep — see the deep audit protocol in the vibe skill (skills/vibe/) for the full specification.

In summary:

  1. Identify all files in scope (from epic commits or recent changes)
  2. Chunk files into batches of 3-5 by line count (<=100 lines -> batch of 5, 101-300 -> batch of 3, >300 -> solo)
  3. Dispatch up to 8 Explore agents in parallel, each with a mandatory 8-category checklist per file (resource leaks, string safety, dead code, hardcoded values, edge cases, concurrency, error handling, HTTP/web security)
  4. Merge all explorer findings into a sweep manifest at .agents/council/sweep-manifest.md
  5. Include sweep manifest in council packet — judges shift to adjudication mode (confirm/reject/reclassify sweep findings + add cross-cutting findings)

Why: Post-mortem council judges exhibit satisfaction bias when reviewing monolithic file sets — they stop at ~10 findings regardless of actual issue count. Per-file explorers with category checklists find 3x more issues, and the sweep manifest gives judges structured input to adjudicate rather than discover from scratch.

Skip conditions:

  • --quick flag -> skip (fast inline path)
  • --skip-sweep flag -> skip (old behavior: judges do pure discovery)
  • No source files in scope -> skip (nothing to audit)

Step 3: Council Validates the Work

Council Verdict:

Run /council with the retrospective preset and always 3 judges:

/council --deep --preset=retrospective validate <epic-or-recent>

Default (3 judges with retrospective perspectives):

  • plan-compliance: What was planned vs what was delivered? What's missing? What was added?
  • tech-debt: What shortcuts were taken? What will bite us later? What needs cleanup?
  • learnings: What patterns emerged? What should be extracted as reusable knowledge?

Post-mortem always uses 3 judges (--deep) because completed work deserves thorough review.

Four-Surface Closure: Validate all four surfaces -- Code, Documentation, Examples, and Proof. A PASS verdict requires all four surfaces addressed, not just code correctness. Read skills/post-mortem/references/four-surface-closure.md for the closure checklist and common gaps.

Timeout: Post-mortem inherits council timeout settings. If judges time out, the council report will note partial results. Post-mortem treats a partial council report the same as a full report — the verdict stands with available judges.

The plan/spec content is injected into the council packet context so the plan-compliance judge can compare planned vs delivered.

With --quick (inline, no spawning):

/council --quick validate <epic-or-recent>

Single-agent structured review. Fast wrap-up without spawning.

With debate mode:

/post-mortem --debate epic-123

Enables adversarial two-round review for post-implementation validation. Use for high-stakes shipped work where missed findings have production consequences. See /council docs for full --debate details.

Advanced options (passed through to council):

  • --mixed — Cross-vendor (Claude + Codex) with retrospective perspectives
  • --preset=<name> — Override with different personas (e.g., --preset=ops for production readiness)
  • --explorers=N — Each judge spawns N explorers to investigate the implementation deeply before judging
  • --debate — Two-round adversarial review (judges critique each other's findings before final verdict)

Step 3.5: Prediction Accuracy (Pre-Mortem Correlation)

When a pre-mortem report exists for the current epic (ls -t .agents/council/*pre-mortem*.md), cross-reference its prediction IDs against actual vibe/implementation findings. Score each as HIT (prediction confirmed), MISS (did not materialize), or SURPRISE (unpredicted issue). Write a ## Prediction Accuracy table in the report. Skip silently if no pre-mortem exists. See re

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/boshu2/agentops --skill post-mortem

The skills CLI fetches post-mortem from GitHub repository boshu2/agentops and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/post-mortem

Reload or restart Cursor to activate post-mortem. Access the skill through slash commands (e.g., /post-mortem) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.649 reviews
  • Hassan Tandon· Dec 24, 2024

    post-mortem is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Li Gill· Dec 12, 2024

    We added post-mortem from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Aisha Johnson· Dec 12, 2024

    Solid pick for teams standardizing on skills: post-mortem is focused, and the summary matches what you get after install.

  • Mateo Gill· Dec 8, 2024

    Useful defaults in post-mortem — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chaitanya Patil· Dec 4, 2024

    Useful defaults in post-mortem — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Mei Verma· Nov 27, 2024

    post-mortem is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Piyush G· Nov 23, 2024

    post-mortem is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Li Rao· Nov 23, 2024

    Solid pick for teams standardizing on skills: post-mortem is focused, and the summary matches what you get after install.

  • Zaid Nasser· Nov 15, 2024

    Useful defaults in post-mortem — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kiara Li· Nov 3, 2024

    post-mortem reduced setup friction for our internal harness; good balance of opinion and flexibility.

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