agentleFS
iadr-deviadr-dev/colab/skills/retrospective/SKILL.md

retrospective

Skill2 starsChanged 7 months agoMITView on GitHub

Post-session learning loop. Compares plan vs actual, extracts novel patterns, updates USER.md and PROJECT.md, promotes draft skills. Run after any session >30 minutes. Triggers on keyword "retro" or auto-suggested by on-stop hook.

69 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use dev-story, team-level and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • lilangMaxSkill

    dev-story

    Read a story file and implement it. Loads the full context (story, GDD requirement, ADR guidelines, control manifest), routes to the right programmer agent for the system and engine, implements the code and test, and confirms each acceptance criterion. The core implementation skill — run after /story-readiness, before /code-review and /story-done.

    Also in 12 projects that use this

  • CoralGameSkill

    team-level

    Orchestrate level design team: level-designer + narrative-director + world-builder + art-director + systems-designer + qa-tester for complete area/level creation.

    Also in 12 projects that use this

  • CoralGameSkill

    team-narrative

    Orchestrate the narrative team: coordinates narrative-director, writer, world-builder, and level-designer to create cohesive story content, world lore, and narrative-driven level design.

    Also in 12 projects that use this

  • lilangMaxSkill

    create-stories

    Break a single epic into implementable story files. Reads the epic, its GDD, governing ADRs, and control manifest. Each story embeds its GDD requirement TR-ID, ADR guidance, acceptance criteria, story type, and test evidence path. Run after /create-epics for each epic.

    Also in 11 projects that use this

  • +7 more from lilangMax
  • +1 more from CoralGame

The file

We split this skill into the rules it gives an agent

7 rules. 0 are common, 3 are distinctive, and 4 are very specific to this project.

  • 0

    Common

    Most of the 58 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 4

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run after significant sessions.

    line 13
  2. Distinctive:

    If it would happen again → capture it.

    line 35
  3. Very specific:

    Update .ohc/PROJECT.md: if:

    line 45
  4. Very specific:

    Update ~/.ohc/USER.md: if:

    line 52
  5. Distinctive:

    Keep under 100 lines — just the pattern and when to use it

    line 60
  6. Very specific:

    If session revealed Cursor-specific learnings: → Use /Generate Cursor Rules to create permanent .mdc rules

    line 67
  7. Very specific:

    Write 3-sentence summary to .ohc/state/sessions/{id}/summary.md:

    line 71

“Common” is measured: kept by at least 6 in 10 of the 58 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Retrospective

The learning loop that prevents the goldfish brain. Run after significant sessions. Takes 5-10 minutes. Pays off every future session.

Step 1: Session Diff

# Node (preferred — zero Python dependency):
node skills/retrospective/scripts/extract-patterns.js

# Python fallback (if Node 18+ unavailable):
python3 skills/retrospective/scripts/extract-patterns.py

Answer:

  • What was planned? (from .ohc/plans/)
  • What actually happened? (from session log)
  • What took longer than expected, and why?
  • What was skipped, and why?
  • What was added that wasn't planned?

Step 2: Extract Patterns

For each thing that went differently than planned: "Is this a one-off, or would this happen again in a similar situation?"

If it would happen again → capture it.

Good patterns to capture:

  • A debugging technique that worked well
  • A library API that was confusing (with correct usage)
  • A project-specific quirk (e.g., "auth service returns both 401 and 403 for expired tokens")
  • A workflow improvement ("next time, check X before starting Y")

Step 3: Update Memory Files

Update .ohc/PROJECT.md if:

  • A project gotcha was discovered
  • The architecture understanding changed
  • A new convention was identified

Format: - [YYYY-MM-DD] {description} → WORKAROUND: {solution}

Update ~/.ohc/USER.md if:

  • A personal workflow preference emerged
  • A strength or weakness pattern appeared

Step 4: Write Draft Skills

For each novel reusable pattern:

  1. Write to .ohc/skills/{kebab-name}.md
  2. Keep under 100 lines — just the pattern and when to use it
  3. Stays in .ohc/skills/ until promoted:
    ohc skill promote {name}

Step 5: Cursor Users — Generate Rules

If session revealed Cursor-specific learnings: → Use /Generate Cursor Rules to create permanent .mdc rules

Step 6: Summarize

Write 3-sentence summary to .ohc/state/sessions/{id}/summary.md:

  1. What was accomplished
  2. What was learned
  3. Recommended next action

See references/retro-template.md for structured format.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.