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 skills add 0xDarkMatter/claude-mods --skill pypi-opsgit clone --depth 1 https://github.com/0xDarkMatter/claude-modsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/0xdarkmatter/claude-mods/pypi-ops)<a href="https://agentmods.dev/skills/0xdarkmatter/claude-mods/pypi-ops"><img src="https://agentmods.dev/badge/skills/0xdarkmatter/claude-mods/pypi-ops/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/0xdarkmatter/claude-mods/pypi-ops"><img src="https://agentmods.dev/badge/skills/0xdarkmatter/claude-mods/pypi-ops.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00074 | $0.03168 |
| Opus 5.5 | $0.00030 | $0.01267 |
| Sonnet 5.5 | $0.00015 | $0.00634 |
| Haiku 4.5 | $0.00007 | $0.00317 |
Grade A, and why
pypi-ops 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 20d 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 — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PyPI Operations
Publish Python packages to PyPI on the 2026 best-practice path: OIDC Trusted
Publishing with signed PEP 740 attestations, no long-lived token to leak. This
skill owns the publish layer (the registry handshake, the first-publish
gotchas, the recovery playbook). General GitHub Actions syntax is ci-cd-ops;
the install-side worm defense is supply-chain-defense; gh/release-page
mechanics are github-ops.
Where this fits — the release pipeline
A release spans several skills; pypi-ops owns the registry step. Chain them:
- Vet dependencies before cutting a release —
supply-chain-defense(cooldown + behavioural scan). The build runs dependency code before it touches your publish credential, so a poisoned build dep can steal the token. - Preflight —
scripts/publish-preflight.sh --build .(this skill). - Bump → tag → push —
git-ops(its push-preflight scans for secrets / forbidden files before the tag goes up). - CI publishes via OIDC — this skill's
assets/publish.yml; you approve at thepypienvironment gate. - Release page (optional, GitHub) —
github-ops, human-reviewed notes.
The one decision: OIDC vs API token
Default to OIDC Trusted Publishing. Reach for a token only when OIDC is impossible (publishing from a non-supported CI, or a one-off local push).
| Trusted Publishing (OIDC) ← default | API token | |
|---|---|---|
| Secret stored | None — short-lived OIDC token minted per run | Long-lived pypi-… token in a secret |
| Leak/phish blast radius | None to steal | Full publish rights until rotated |
| Provenance | PEP 740 attestations (signed, verifiable) | None by default |
| Setup | One-time publisher registration on PyPI | Generate token + store secret |
| Best for | All CI/CD releases | Legacy CI, emergency local upload |
If a repo currently uses a token, migrating to OIDC is strictly an upgrade — see references/trusted-publishing.md.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/publish.yml 4.4 KB
- references/recovery-playbook.md 5.1 KB
- references/trusted-publishing.md 4.7 KB
- references/uv-publish.md 2.9 KB
- scripts/check-action-pins.py 6.2 KB runs code
- scripts/diagnose-publish.sh 6.7 KB runs code
- scripts/publish-preflight.sh 10 KB runs code
- tests/run.sh 7.1 KB runs code
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.
- 20d ago Changed 7ec138806710
- 1mo ago First seen · 242 lines · 74 tokens per session scan A d481bafc6ca5
pypi-ops is a skill published in the GitHub repository 0xDarkMatter/claude-mods (76 stars, last pushed 13d ago), licensed MIT. It adds 74 tokens to every session and 3,168 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
agent-django-fastapi-expert
Specialist subagent: Django ORM, Django REST Framework, FastAPI async patterns, and Pydantic v2 specialist. Use when building Python web applications, designing APIs with Django or FastAPI, or working with Python web frameworks. Trigger phrases: Django, FastAPI, DRF, Django REST Framework, Pydantic, ORM, queryset…
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
agent-python-expert
Specialist subagent: Python best practices, type hints, async patterns, and ecosystem expertise. Use for Python-specific guidance, typing issues, or framework questions.
py-debug
Python debugging expert. Use when the user needs to debug, profile, or trace Python code — e.g. "how do I debug this", "find the memory leak", "why is this slow", "add a breakpoint", "profile this function".
python-logging-best-practices
Python logging with loguru, structlog, and orjson. TRIGGERS - loguru, structlog, structured logging.
python-memory-safe-scripts
Memory-safe Python script patterns for long-running processes under systemd MemoryMax constraints. Covers allocator purge (mimalloc/glibc malloctrim), HTTP response lifecycle, DataFrame cleanup, thread-local connection reuse, and periodic GC cadence. Battle-tested through 5 OOM optimization cycles on production GPU…