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/songshgeo/python-project-template/quality-checknpx skills add SongshGeo/Python-Project-Template --skill quality-checkgit clone --depth 1 https://github.com/SongshGeo/Python-Project-TemplateWhat 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 | $0.00064 | $0.00284 |
| Opus 5 | $0.00032 | $0.00142 |
| Sonnet 5 | $0.00013 | $0.00057 |
| Haiku 4.5 | $0.00006 | $0.00028 |
Grade A, and why
quality-check 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.
What it actually says
quality-check
Run the repository's quality gate and report results clearly. uv is required.
Steps
- Tests
make test # == uv run pytest - Pre-commit hooks (black, ruff, isort, flake8, mypy, interrogate,
nbstripout, file fixers):
uv run pre-commit run --all-files - Docstring coverage (if you want it standalone):
uv run interrogate -v - Optional — multi-version matrix (only if asked; it is slow):
make tox # Python 3.10–3.13
Reporting
- Summarize each stage as pass/fail.
- For failures, quote the relevant output and propose the concrete fix; offer to apply it.
- Do not mark the work "done" while any stage is red.
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 · 36 lines · 64 tokens per session scan A 0e716c2cf581
quality-check is a skill published in the GitHub repository SongshGeo/Python-Project-Template (8 stars, last pushed 2mo ago), licensed MIT. It adds 64 tokens to every session and 284 once invoked, about $0.0003 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.
Other skills, from other repositories
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brainstorming
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auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…