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 nimadorostkar/Claude-Skills-collection --skill pythongit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/nimadorostkar/claude-skills-collection/python)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/python"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/python/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/nimadorostkar/claude-skills-collection/python"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/python.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 | $0.00108 | $0.01136 |
| Opus 5.5 | $0.00043 | $0.00454 |
| Sonnet 5.5 | $0.00022 | $0.00227 |
| Haiku 4.5 | $0.00011 | $0.00114 |
Grade A, and why
python 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 9d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python
Purpose
Write production Python that is type-safe, async-first, and testable. This skill sets a single quality bar — annotated, linted, tested — and applies it consistently to new code and to code being modernized.
When to Use
- Writing new Python modules, packages, or services.
- Adding type coverage to an untyped or partially typed codebase.
- Converting blocking I/O to
asyncio, or debugging async behavior. - Standing up a pytest suite, fixtures, or parametrized tests.
- Modernizing Python 2-era or pre-3.10 idioms.
Capabilities
- Full type annotation, including generics,
Protocol,TypedDict, andParamSpec. - Async design: task groups, timeouts, cancellation, structured concurrency.
- Data modeling with
dataclasses,enum, and Pydantic when validation is needed. - Test authoring: fixtures, factories, mocking, property-based tests via Hypothesis.
- Tooling configuration:
pyproject.toml, ruff, mypy, uv or Poetry. - Profiling and hot-path optimization.
Inputs
- Source files or a package path.
- Target Python version (default: 3.12).
- Existing tooling config, if any.
- Runtime constraints: sync vs async, framework, deployment target.
Outputs
- Type-annotated source that passes
mypy --strict. - A pytest suite with meaningful assertions, not coverage padding.
- A
pyproject.tomlsection configuring ruff and mypy. - A short summary of behavioral changes when refactoring.
Workflow
- Survey — Read the module and its imports. Identify the runtime model (sync, async, threaded) and existing conventions. Do not fight established conventions without a reason.
- Model the data — Define dataclasses, enums, and protocols before writing logic. Type the boundaries first.
- Implement — Write the smallest correct version. Prefer standard library over dependencies.
- Test — Cover the contract and the failure modes, not the implementation details.
- Gate — Run
ruff check --fix,ruff format,mypy --strict,pytest. Fix each failure and re-run until all four are clean.
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.
- 9d ago Changed · +53 tokens per session 1f3f1f8585e3
- 1mo ago First seen · 118 lines · 55 tokens per session scan A e0c85b4c0fdd
python is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (30 stars, last pushed 9d ago), licensed MIT. It adds 108 tokens to every session and 1,136 once invoked, about $0.0004 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.
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