claude-skills is a collection of specialized skills that extends Claude Code for full-stack development. Developers use it for programming languages, frameworks, infrastructure, APIs, testing, DevOps, security, data and machine learning, platform tasks, and project workflows.
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 Jeffallan/claude-skills --skill python-progit clone --depth 1 https://github.com/Jeffallan/claude-skillsWrote 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/jeffallan/claude-skills/python-pro)<a href="https://agentmods.dev/skills/jeffallan/claude-skills/python-pro"><img src="https://agentmods.dev/badge/skills/jeffallan/claude-skills/python-pro/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/jeffallan/claude-skills/python-pro"><img src="https://agentmods.dev/badge/skills/jeffallan/claude-skills/python-pro.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00083 | $0.01473 |
| Opus 5.5 | $0.00033 | $0.00589 |
| Sonnet 5.5 | $0.00017 | $0.00295 |
| Haiku 4.5 | $0.00008 | $0.00147 |
Grade A, and why
python-pro 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 16d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- python-pro — 98% identical, 5 lines differ
- python-pro — 98% identical, 5 lines differ
- python-pro — 98% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Pro
Modern Python 3.11+ specialist focused on type-safe, async-first, production-ready code.
When to Use This Skill
- Writing type-safe Python with complete type coverage
- Implementing async/await patterns for I/O operations
- Setting up pytest test suites with fixtures and mocking
- Creating Pythonic code with comprehensions, generators, context managers
- Building packages with Poetry and proper project structure
- Performance optimization and profiling
Core Workflow
- Analyze codebase — Review structure, dependencies, type coverage, test suite
- Design interfaces — Define protocols, dataclasses, type aliases
- Implement — Write Pythonic code with full type hints and error handling
- Test — Create comprehensive pytest suite with >90% coverage
- Validate — Run
mypy --strict,black,ruff- If mypy fails: fix type errors reported and re-run before proceeding
- If tests fail: debug assertions, update fixtures, and iterate until green
- If ruff/black reports issues: apply auto-fixes, then re-validate
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Type System | references/type-system.md |
Type hints, mypy, generics, Protocol |
| Async Patterns | references/async-patterns.md |
async/await, asyncio, task groups |
| Standard Library | references/standard-library.md |
pathlib, dataclasses, functools, itertools |
| Testing | references/testing.md |
pytest, fixtures, mocking, parametrize |
| Packaging | references/packaging.md |
poetry, pip, pyproject.toml, distribution |
Constraints
MUST DO
- Type hints for all function signatures and class attributes
- PEP 8 compliance with black formatting
- Comprehensive docstrings (Google style)
- Test coverage exceeding 90% with pytest
- Use
X | Noneinstead ofOptional[X](Python 3.10+) - Async/await for I/O-bound operations
- Dataclasses over manual init methods
- Context managers for resource handling
What ships with it
5 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.
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.
- 16d ago Changed · +3 lines b94b59450d73
- 1mo ago First seen · 180 lines · 83 tokens per session scan A 83acbb761bc3
python-pro is a skill published in the GitHub repository Jeffallan/claude-skills (11,943 stars, last pushed 4d ago), licensed MIT. It adds 83 tokens to every session and 1,473 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 2026-08-30.
Other skills, from other repositories
dask
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astropy
Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
optimize-for-gpu
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS…
polars
High-performance DataFrame library for Python ETL, analytics, and pandas migration. It supports expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.
bioservices
Provides a Python interface to bioinformatics services including UniProt, KEGG, ChEMBL, Reactome, QuickGO, and UniChem. Used for cross-database protein annotation, pathway retrieval, chemical identifier mapping, and integrated biological data workflows with BioServices.