Install
$ agentstack add skill-tomzx-agents-create-learnings ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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 frontmatterissuefield 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
- Gather context: what was delivered, timeline, team involved, and any notable events.
- Reflect on what went well (practices worth repeating and amplifying).
- Reflect on what didn't go well, identifying root causes not just symptoms.
- Identify concrete process improvements with owners and dates.
- Capture technical insights: decisions that paid off and decisions to revisit.
- Distill actionable next steps.
- Write the output to
.sdlc/knowledge/learnings/N-.mdwhere 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.
- Author: tomzx
- Source: tomzx/agents
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.