Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/tomzx/agents/create-learningsnpx skills add tomzx/agents --skill create-learningsgit clone --depth 1 https://github.com/tomzx/agentsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00022 | $0.00732 |
| Opus 5 | $0.00011 | $0.00366 |
| Sonnet 5 | $0.00004 | $0.00146 |
| Haiku 4.5 | $0.00002 | $0.00073 |
Grade A, and why
create-learnings scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Learnings
Facilitates a retrospective to capture actionable learnings after completing a feature, sprint, or project. Produces a structured document covering what went well, what didn't, process improvements, technical insights, and next actions.
Prerequisites
- Apply the shared SDLC conventions in
skills/sdlc/references/shared.md. - If no argument is provided, locate the feature directory under
.sdlc/features/whose frontmatterissuefield references$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-<slug>.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.
In the same emission, list the artifact under artifacts: (.sdlc/knowledge/learnings/N-<slug>.md).
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).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 67 lines · 22 tokens per session scan A 5194cbd2e476
create-learnings is a skill published in the GitHub repository tomzx/agents (5 stars, last pushed 5d ago), licensed MIT. It adds 22 tokens to every session and 732 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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auto-perf-optimize
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chat-perf
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chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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