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/SongshGeo/Python-Project-TemplateWrote 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/songshgeo/python-project-template/cursorrules)<a href="https://agentmods.dev/rules/songshgeo/python-project-template/cursorrules"><img src="https://agentmods.dev/badge/rules/songshgeo/python-project-template/cursorrules/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/songshgeo/python-project-template/cursorrules"><img src="https://agentmods.dev/badge/rules/songshgeo/python-project-template/cursorrules.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 | $0.00456 | $0.00456 |
| Opus 5.5 | $0.00182 | $0.00182 |
| Sonnet 5.5 | $0.00091 | $0.00091 |
| Haiku 4.5 | $0.00046 | $0.00046 |
Grade A, and why
cursorrules 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.
This is a copy
86% identical to python-cursorrules-prompt-file-best-practices — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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/uv and virtual environments.
- Code style consistency using Ruff.
- CI/CD implementation with GitHub Actions or GitLab CI.
AI-friendly coding practices:
- You provide code snippets and explanations tailored to these principles, optimizing for clarity and AI-assisted development.
Follow the following rules:
- For any Python file, ALWAYS add typing annotations to each function or class. Include explicit return types (including None where appropriate). Add descriptive docstrings to all Python functions and classes.
- Please follow PEP 257 docstring conventions. Update existing docstrings as needed.
- Make sure you keep any comments that exist in a file.
- When writing tests, ONLY use pytest or pytest plugins (not unittest). All tests should have typing annotations. Place all tests under ./tests. Create any necessary directories. If you create packages under ./tests or ./src/<package_name>, be sure to add an init.py if one does not exist.
All tests should be fully annotated and should contain docstrings. Be sure to import the following if TYPE_CHECKING: from _pytest.capture import CaptureFixture from _pytest.fixtures import FixtureRequest from _pytest.logging import LogCaptureFixture from _pytest.monkeypatch import MonkeyPatch from pytest_mock.plugin import MockerFixture
Before you begin, carefully read all documents in the top-level memory-bank/
(the shared memory bank used by both Cursor and Claude Code). Keep those
documents up to date as the project evolves.
Avoid using "try...except" syntax to prevent exceptions from being silenced.
Note: these rules are kept in sync with the repository's CLAUDE.md, which is
the authoritative source for coding standards and the skill/workflow index. If
the two ever diverge, CLAUDE.md wins.
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 · 37 lines · 456 tokens per session scan A c6955b8db13c
cursorrules is a cursor rule published in the GitHub repository SongshGeo/Python-Project-Template (8 stars, last pushed 3mo ago), licensed MIT. It adds 456 tokens to every session, about $0.0018 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to python-cursorrules-prompt-file-best-practices, differing in 14 lines, and is treated as a copy.
Other cursor rules, from other repositories
python_tests
We use the unit tests to cover internal behavior that can work without the web / backend counterpart. We aim for 95%+ unit test coverage of our Python code in lib/streamlit.
python-llm-ml-workflow-cursorrules-prompt-file
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Python best practices and patterns for modern software development with Flask and SQLite.
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Cursor rules for Python development with containerization integration.
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Definitive guidelines for writing maintainable, performant, and secure Django applications, emphasizing modern best practices, clear code organization, and efficient patterns.
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Definitive guidelines for building robust, maintainable, and visually appealing Python CLI applications using the rich library.