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/ludo-technologies/python-best-practices/toolingnpx skills add ludo-technologies/python-best-practices --skill toolinggit clone --depth 1 https://github.com/ludo-technologies/python-best-practicesWhat 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 | $0.00060 | $0.00921 |
| Opus 5 | $0.00030 | $0.00461 |
| Sonnet 5 | $0.00012 | $0.00184 |
| Haiku 4.5 | $0.00006 | $0.00092 |
Grade A, and why
tooling 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 yesterday.
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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Tooling
A comprehensive guide to Python development tools. Configuration best practices for analysis, linters, type checkers, formatters, test frameworks, and package managers.
Why These Tools Matter
To write high-quality Python code, we recommend adopting these tools:
- pyscn - Detect dead code, duplicates, and circular dependencies to prevent technical debt
- ruff - Catch bugs and style violations early with fast static analysis
- mypy - Find errors before runtime with type checking, also improves IDE completion
- pytest - Build confidence in changes with reliable tests
- uv - Improve developer experience with fast dependency management
Integrating these into CI/CD reduces code review burden and maintains consistent quality.
Categories
Analysis [HIGH]
Structural code analysis for quality assessment.
| Rule | Description |
|---|---|
| analysis-pyscn | Dead code, clones, dependencies, complexity with pyscn |
Linting [CRITICAL]
Static code analysis with ruff for consistent, high-quality code.
| Rule | Description |
|---|---|
| lint-ruff | Fast, comprehensive linting with ruff |
Type Checking [HIGH]
Static type checking with mypy for type safety and better IDE support.
| Rule | Description |
|---|---|
| type-mypy | Static type checking with mypy |
Formatting [HIGH]
Consistent code formatting with ruff format and import sorting.
| Rule | Description |
|---|---|
| fmt-ruff | Code formatting and import sorting with ruff |
Testing [HIGH]
Test framework configuration with pytest for reliable testing.
| Rule | Description |
|---|---|
| test-pytest | Testing with pytest, fixtures, and coverage |
Package Management [MEDIUM]
Modern Python packaging with uv and pyproject.toml.
| Rule | Description |
|---|---|
| pkg-uv | Fast package management with uv |
| pkg-pyproject | Project configuration with pyproject.toml |
What ships with it
11 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.
- yesterday First seen · 131 lines · 60 tokens per session scan A f753bddb0776
tooling is a skill published in the GitHub repository ludo-technologies/python-best-practices (18 stars, last pushed 3d ago), licensed MIT. It adds 60 tokens to every session and 921 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 2026-08-30.
Other skills, from other repositories
notebooklm
Complete API for Google NotebookLM - full programmatic access including features not in the web UI. Create notebooks, add sources, generate all artifact types, download in multiple formats. Activates on explicit /notebooklm or intent like "create a podcast about X".
ppt-master
AI-driven multi-format SVG content generation system. Converts source documents (PDF/DOCX/URL/Markdown) into high-quality SVG pages and exports to PPTX through multi-role collaboration. Use when user asks to "create PPT", "make presentation", "生成PPT", "做PPT", "制作演示文稿", or mentions "ppt-master".
markitdown-skill
本文件属于公开技能 AIProjects。本节为中文主体补充说明，目标是让中文读者在不依赖英文背景的情况下，理解本文件的作用、阅读路径、关键约束和验收标准。 本文件的相对路径是 markitdown-skill/SKILL.md，属于 AIProjects 的参考/工作流/模板/脚本/资产之一。 使用方法：先看本中文主体补充；再按需进入下方原始英文细则、表格、清单、JSON、代码或命令；最后按验收要点核对产物。 中文部分只做解释，不替换、不重写、不翻译任何原始字段名、参数名、命令名、文件名、URL、环境变量、JSON/YAML 键、表格技术列头、模板占位或代码块。…
vision
识别图片内容。当用户发送图片、截图、报错图，或要求分析某张本地图片时使用。优先调用 visionanalyze（本地图片路径）或 visionclipboard（剪贴板图片）。.
video-notes-generator
Use when user wants to summarize, analyze, or generate notes from a video URL. Converts video content into structured Markdown notes with timestamps, extracted visual frames, native multimodal image observations, and AI summaries. Supports Bilibili, YouTube, Douyin, Kuaishou, and local files.
vision
识别图片内容。当用户发送图片、截图、报错图，或要求分析某张本地图片时使用。先定位图片文件路径，再运行 vision.py 脚本获取文字描述。.