Retro Skill

YOU MUST EXECUTE THIS WORKFLOW. Do not just describe it.

Extract learnings from completed work, propose proactive improvements, and feed the knowledge flywheel.

Flags

--quick "text"

.agents/learnings/

--vibe-results <path>

/vibe

Quick Mode

Given

/retro --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: retro-quick
date: YYYY-MM-DD
---

# Learning: <Short Title>

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

## What We Learned

<user's insight text>

## Source

Quick capture via `/retro --quick`

This skips the pool pipeline — writes directly to learnings, not

.agents/knowledge/pending/

Quick Step 3: Confirm

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

For deeper reflection, use `/retro` without --quick.

Done. Return immediately after confirmation.

Full Retrospective

Execution Steps

Given

/retro [topic] [--vibe-results <path>]

Step 1: Identify What to Retrospect

If vibe results path provided: Read and incorporate validation findings:

Tool: Read
Parameters:
  file_path: <vibe-results-path>

This allows post-mortem to pass validation context without re-running vibe.

If topic provided: Focus on that specific work.

If no topic: Look at recent activity:

# Recent commits
git log --oneline -10 --since="7 days ago"

# Recent issues closed
bd list --status closed --since "7 days ago" 2>/dev/null | head -5

# Recent research/plans
ls -lt .agents/research/ .agents/plans/ 2>/dev/null | head -5

Step 2: Gather Context

Read relevant artifacts:

• Research documents
• Plan documents
• Commit messages
• Code changes

Use the Read tool and git commands to understand what was done.

Step 3: Identify Learnings

If vibe results were provided, incorporate them:

• Extract learnings from CRITICAL and HIGH findings
• Note patterns that led to issues
• Identify anti-patterns to avoid

Ask these questions:

What went well?

• What approaches worked?
• What was faster than expected?
• What should we do again?

What went wrong?

• What failed?
• What took longer than expected?
• What would we do differently?
• (Include vibe findings if provided)

What did we discover?

• New patterns found
• Codebase quirks learned
• Tool tips discovered
• Debugging insights

Step 4: Extract Actionable Learnings

For each learning, capture:

• ID: L1, L2, L3...
• Category: debugging, architecture, process, testing, security
• What: The specific insight
• Why it matters: Impact on future work
• Confidence: high, medium, low

Step 5: Write Learnings

Write to:

.agents/learnings/YYYY-MM-DD-<topic>.md

---
id: learning-YYYY-MM-DD-<slug>
type: learning
date: YYYY-MM-DD
category: <category>
confidence: <high|medium|low>
---

# Learning: <Short Title>

## What We Learned

<1-2 sentences describing the insight>

## Why It Matters

<1 sentence on impact/value>

## Source

<What work this came from>

---

# Learning: <Next Title>

**ID**: L2
...

Step 5.5: Classify Learning Scope

For each learning extracted in Step 5, classify:

Question: "Does this learning reference specific files, packages, or architecture in THIS repo? Or is it a transferable pattern that helps any project?"

• Repo-specific → Write to

  .agents/learnings/

  (existing behavior from Step 5). Use

  git rev-parse --show-toplevel

  to resolve repo root — never write relative to cwd.
• Cross-cutting/transferable → Rewrite to remove repo-specific context (file paths, function names, package names), then:
  1. Write abstracted version to

     ~/.agents/learnings/YYYY-MM-DD-<slug>.md

     (NOT local — one copy only)
  2. Run abstraction lint check:

     file="<path-to-written-global-file>"
     grep -iEn '(internal/|cmd/|\.go:|/pkg/|/src/|AGENTS\.md|CLAUDE\.md)' "$file" 2>/dev/null
     grep -En '[A-Z][a-z]+[A-Z][a-z]+\.(go|py|ts|rs)' "$file" 2>/dev/null
     grep -En '\./[a-z]+/' "$file" 2>/dev/null
     

     If matches: WARN user with matched lines, ask to proceed or revise. Never block the write.

Note: Each learning goes to ONE location (local or global). No

promoted_to

/retro

Example abstraction:

• Local: "Athena's validate package needs O_CREATE|O_EXCL for atomic claims because Zeus spawns concurrent workers"
• Global: "Use O_CREATE|O_EXCL for atomic file creation when multiple processes may race on the same path"

Step 5.6: Compile Constraint Templates

For each extracted learning scoring >= 4/5 on actionability AND tagged "constraint" or "anti-pattern", run

bash hooks/constraint-compiler.sh <learning-path>

# Compile high-scoring constraint/anti-pattern learnings into enforcement templates
for f in .agents/learnings/YYYY-MM-DD-*.md; do
    [ -f "$f" ] || continue
    bash hooks/constraint-compiler.sh "$f" 2>/dev/null || true
done

This produces draft constraint templates in

.agents/constraints/

ao constraint activate <id>

Step 6: Write Retro Summary

Write to:

.agents/retros/YYYY-MM-DD-<topic>.md

---
id: retro-YYYY-MM-DD-<topic-slug>
type: retro
date: YYYY-MM-DD
---

# Retrospective: <Topic>

**Scope:** <what work was reviewed>

## Summary
<1-2 sentence overview>

## What Went Well
- <thing 1>
- <thing 2>

## What Could Be Improved
- <improvement 1>
- <improvement 2>

## Learnings Extracted
- L1: <brief>
- L2: <brief>

See: `.agents/learnings/YYYY-MM-DD-<topic>.md`

## Proactive Improvement Agenda

| # | Area | Improvement | Priority | Horizon | Effort | Evidence |
|---|------|-------------|----------|---------|--------|----------|
| 1 | repo / execution / CI | <improvement> | P0/P1/P2 | now/next-cycle/later | S/M/L | <retro evidence> |

### Recommended Next /rpi
/rpi "<highest-value item>"

## Action Items
- [ ] <any follow-up needed>

Step 6.5: Proactive Improvement Agenda (MANDATORY)

After writing the retro summary, use the full context you just gathered to propose concrete improvements.

Ask explicitly:

1. Repo: What should we improve in the codebase/contracts/docs to reduce future defects?
2. Execution: What should we improve in planning/implementation/review workflow to increase throughput?
3. CI/Automation: What should we improve in validation gates/tooling to reduce noise and catch regressions earlier?

Requirements:

• Propose at least 5 items total.
• Cover all three areas above (repo, execution, CI/automation).
• Include at least 1 quick win (small, low-risk, same-session viable).
• For each item include:

  priority

  (P0/P1/P2),

  horizon

  (now/next-cycle/later),

  effort

  (S/M/L), and one-line rationale tied to retro evidence.
• Mark one item as Recommended Next /rpi.

Write this into the retro file under:

## Proactive Improvement Agenda

| # | Area | Improvement | Priority | Horizon | Effort | Evidence |
|---|------|-------------|----------|---------|--------|----------|
| 1 | CI | <improvement> | P0 | now | S | <retro evidence> |

### Recommended Next /rpi
/rpi "<highest-value item>"

Step 7: Feed the Knowledge Flywheel (auto-extract)

# If ao available, index via forge, close session, and trigger flywheel
if command -v ao &>/dev/null; then
  ao forge markdown .agents/learnings/YYYY-MM-DD-*.md 2>/dev/null
  echo "Learnings indexed in knowledge flywheel"

  # Apply feedback from completed tasks to associated learnings
  ao task-feedback 2>/dev/null
  echo "Task feedback applied"

  # Close session and trigger full flywheel close-loop
  ao session close 2>/dev/null || true
  ao flywheel close-loop --quiet 2>/dev/null || true
  echo "Session closed, flywheel loop triggered"

  # Sync insights to MEMORY.md immediately (don't wait for session end)
  ao notebook update --quiet 2>/dev/null || true

  # Flag stale constraints for retirement
  ao constraint review 2>/dev/null || true
else
  # Learnings are already written to .agents/learnings/ by Step 5.
  # Without ao CLI, grep-based search in /research and /inject
  # will find them directly — no copy to pending needed.

  # Build lightweight keyword index for faster search
  mkdir -p .agents/ao
  for f in .agents/learnings/YYYY-MM-DD-*.md; do
    [ -f "$f" ] || continue
    TITLE=$(head -1 "$f" | sed 's/^# //')
    echo "{\"file\": \"$f\", \"title\": \"$TITLE\", \"keywords\": [], \"timestamp\": \"$(date -Iseconds)\"}" >> .agents/ao/search-index.jsonl
  done
  echo "Learnings indexed locally (ao CLI not available — grep-based search active)"
fi

This auto-extraction step ensures every retro feeds the flywheel without requiring the user to remember manual commands.

Step 8: Report to User

Tell the user:

1. Number of learnings extracted
2. Key insights (top 2-3)
3. Location of retro and learnings files
4. Knowledge has been indexed for future sessions
5. Top proactive improvements (top 3) + recommended next

   /rpi

Key Rules

• Be specific - "auth tokens expire" not "learned about auth"
• Be actionable - learnings should inform future decisions
• Cite sources - reference what work the learning came from
• Write both files - retro summary AND detailed learnings
• Be proactive - always produce repo + execution + CI improvements from gathered context
• Index knowledge - make it discoverable

The Flywheel

Learnings feed future research:

Work → /retro → improvements + learnings → ao forge markdown → /research finds it

Future sessions start smarter because of your retrospective.

Examples

Retrospective After Implementation

User says:

/retro

What happens:

1. Agent looks at recent activity via

   git log --oneline -10

2. Agent finds 8 commits related to authentication refactor
3. Agent reads commit messages, code changes, and related issue in beads
4. Agent asks: What went well? What went wrong? What was discovered?
5. Agent identifies 4 learnings: L1 (token expiry pattern), L2 (middleware ordering matters), L3 (test coverage caught edge case), L4 (documentation prevents support load)
6. Agent writes learnings file to

   .agents/learnings/2026-02-13-auth-refactor.md

7. Agent writes retro summary to

   .agents/retros/2026-02-13-auth-refactor.md

8. Agent runs

   ao forge markdown

   to add learnings to knowledge base

Result: 4 learnings extracted and indexed, retro summary documents what went well and improvements needed.

Post-Mortem with Vibe Results

User says:

/retro --vibe-results .agents/council/2026-02-13-vibe-api.md

What happens:

1. Agent reads vibe results file showing 2 CRITICAL and 3 HIGH findings
2. Agent extracts learnings from validation findings (race condition pattern, missing input validation)
3. Agent reviews recent commits for context
4. Agent creates 6 learnings: 2 from vibe findings (what to avoid), 4 from successful patterns (what to repeat)
5. Agent writes both learnings and retro files
6. Agent indexes knowledge automatically via ao forge

Result: Vibe findings incorporated into learnings, preventing same issues in future work.

Troubleshooting

/retro "planning process improvements"

.agents/ao/search-index.jsonl

.agents/learnings/

Reference Documents

• references/context-gathering.md
• references/output-templates.md