Agentic Plugin Marketplace is a collection of reusable plugins, agents, skills, commands, and rules for coding-agent tools including Claude Code, Codex CLI, Cursor, OpenCode, Antigravity CLI, and GitHub Copilot. It is for developers assembling agentic workflows across multiple harnesses from shared Markdown sources, and the catalogue entries are examples or subsets of those workflow components.
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.
git clone --depth 1 https://github.com/wshobson/agentsWrote 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/rules/wshobson/agents/010-python-tooling)<a href="https://agentmods.dev/rules/wshobson/agents/010-python-tooling"><img src="https://agentmods.dev/badge/rules/wshobson/agents/010-python-tooling/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/rules/wshobson/agents/010-python-tooling"><img src="https://agentmods.dev/badge/rules/wshobson/agents/010-python-tooling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.00183 |
| Opus 5.5 | $0.00000 | $0.00073 |
| Sonnet 5.5 | $0.00000 | $0.00037 |
| Haiku 4.5 | $0.00000 | $0.00018 |
Grade A, and why
010-python-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 1mo 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
Python tooling
This project uses the Astral Rust toolchain:
- uv for package management. Run
uv run <cmd>to execute inside the venv,uv add <pkg>to install. - ruff for linting and formatting.
uv run ruff check ./uv run ruff format .. - ty for type checking.
uv run ty check ..
Do not use pip, mypy, or black. Do not edit requirements.txt for Python deps — those belong in pyproject.toml.
Python ≥ 3.12 is required. Use modern syntax (X | Y, list[T], structural pattern matching).
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.
- 1mo ago First seen · 17 lines · 0 tokens per session scan A 3571a78350ed
010-python-tooling is a cursor rule published in the GitHub repository wshobson/agents (40,863 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 183 tokens. 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 cursor rules, from other repositories
continuum
Continuum Agent Framework — API reference and conventions for the orchestrator source tree.
python
Rules applied when working in Python files.
cursorrules
You are building an AI/ML project with Python. The project uses PyTorch for model training, handles data pipelines with proper validation, tracks experiments systematically, and follows production ML engineering practices. Code is type-hinted, tested, and reproducible.
10-python-standards
General Python engineering standards.
10-backend-fastapi
FastAPI backend rules.
django
Django: models, views, ORM best practices.