PatrickJS/awesome-cursorrules is a collection of Markdown rule files that give Cursor AI editor project-specific instructions about code, frameworks, workflows, and standards. Developers use it to find reusable guidance for shaping Cursor’s behavior in different kinds of software projects.
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/PatrickJS/awesome-cursorrulesWrote 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/patrickjs/awesome-cursorrules/python-projects-guide-cursorrules-prompt-file)<a href="https://agentmods.dev/rules/patrickjs/awesome-cursorrules/python-projects-guide-cursorrules-prompt-file"><img src="https://agentmods.dev/badge/rules/patrickjs/awesome-cursorrules/python-projects-guide-cursorrules-prompt-file.svg" alt="Measured on agentmods" 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 | $0.00211 | $0.00211 |
| Opus 5 | $0.00105 | $0.00105 |
| Sonnet 5 | $0.00042 | $0.00042 |
| Haiku 4.5 | $0.00021 | $0.00021 |
Grade A, and why
python-projects-guide-cursorrules-prompt-file 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 3d 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
You are an AI assistant specialized in Python development. Your approach emphasizes:
- Clear project structure with separate directories for source code, tests, docs, and config.
- Modular design with distinct files for models, services, controllers, and utilities.
- Configuration management using environment variables.
- Robust error handling and logging, including context capture.
- Comprehensive testing with pytest.
- Detailed documentation using docstrings and README files.
- Dependency management via https://github.com/astral-sh/rye and virtual environments.
- Code style consistency using Ruff.
- CI/CD implementation with GitHub Actions or GitLab CI.
- AI-friendly coding practices:
- Descriptive variable and function names
- Type hints
- Detailed comments for complex logic
- Rich error context for debugging
You provide code snippets and explanations tailored to these principles, optimizing for clarity and AI-assisted development.
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.
- 3d ago First seen · 24 lines · 211 tokens per session scan A 31781a6b9949
python-projects-guide-cursorrules-prompt-file is a cursor rule published in the GitHub repository PatrickJS/awesome-cursorrules (40,734 stars, last pushed 3mo ago), licensed CC0-1.0. It adds 211 tokens to every session, about $0.0011 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 2026-09-03.
Other cursor rules, from other repositories
django
Django: models, views, ORM best practices.
python
Python best practices: type hints, pathlib, pytest, clean error handling.
pydantic
Pydantic: BaseModel, validators, Field, Settings.
pytest
You are an expert in pytest testing. Follow these rules.
pytorch
PyTorch: neural networks, model training, GPU optimization.
ruff
Ruff: linting, formatting, rule selection, configuration.