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SKILL verified MIT Self-run

Create Learnings

skill-tomzx-agents-create-learnings · by tomzx

Capture learnings after completing a feature, sprint, or project in a structured retrospective format.

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Install

$ agentstack add skill-tomzx-agents-create-learnings

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
0 installs to date
no reviews yet
1mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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How agent discovery & health will work →
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About

Create Learnings

Facilitates a retrospective to capture actionable learnings after completing a feature, sprint, or project. Produces a structured document covering what went well, what didn't, process improvements, technical insights, and next actions.

Prerequisites

  • Apply the shared SDLC conventions in skills/sdlc/references/shared.md.
  • If no argument is provided, locate the feature directory under .sdlc/features/ whose frontmatter issue field references $ISSUE_NUMBER.
  • A completed feature, sprint, or project to reflect on
  • Context about what was built, how long it took, and any notable events
  • If any files exist under .sdlc/knowledge/assumptions/ or .sdlc/knowledge/decisions/ for this feature, review them for context.

Steps

  1. Gather context: what was delivered, timeline, team involved, and any notable events.
  2. Reflect on what went well (practices worth repeating and amplifying).
  3. Reflect on what didn't go well, identifying root causes not just symptoms.
  4. Identify concrete process improvements with owners and dates.
  5. Capture technical insights: decisions that paid off and decisions to revisit.
  6. Distill actionable next steps.
  7. Write the output to .sdlc/knowledge/learnings/N-.md where N is the next available sequence number in that directory.

Output Format

Use the template at skills/sdlc/templates/knowledge/learning.md (copied to .sdlc/templates/knowledge/learning.md by /initialize-sdlc-directory; use the project's customized copy if present). Write the result to the artifact path named in the steps above.

Outcome

If $OUTCOME_YAML is set, emit verdict: approved there per skills/sdlc/references/shared.md once the learnings artifact is written.

Example Usage

Scenario 1: Feature retrospective A payment feature took 3 weeks instead of 2. Learnings: the third-party API was underdocumented (add a spike phase to future plans involving new integrations), automated integration tests caught 4 regressions early (keep and expand), the spec was changed mid-implementation (add a spec-freeze milestone to the plan template).

Scenario 2: Sprint retrospective End-of-sprint review with the team. What went well: daily standups kept everyone aligned. What didn't: unclear task definitions led to rework. Action item: run /create-tasks-decomposition before each sprint starts.

Completion Checklist

Before handing off to review, confirm:

  • [ ] "What could improve" entries identify root causes, not just symptoms
  • [ ] Action items are specific, owned, and dated

Self-check the draft against the [review-learnings checklist](../review-learnings/SKILL.md) and fix what you can, so review finds less to flag.

Useful Commands Reference

No CLI commands required. This skill operates on information provided in context.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.