awesome-cursor-rules-mdc is a generator that creates Cursor MDC rule files from structured library information, using semantic search and language models to gather and organize guidance. Developers use it to produce reusable rules for libraries in Cursor, and the catalogue includes 200 of those rules.
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/sanjeed5/awesome-cursor-rules-mdcWrote 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/sanjeed5/awesome-cursor-rules-mdc/mypy)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/mypy"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/mypy/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/sanjeed5/awesome-cursor-rules-mdc/mypy"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/mypy.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 | $0.01436 | $0.01436 |
| Opus 5.5 | $0.00574 | $0.00574 |
| Sonnet 5.5 | $0.00287 | $0.00287 |
| Haiku 4.5 | $0.00144 | $0.00144 |
Grade A, and why
mypy 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mypy Best Practices
mypy is the definitive static type checker for Python. Adhering to these guidelines ensures your codebase is robust, maintainable, and catches type errors before runtime.
1. Configuration & Integration
Always use a shared pyproject.toml for mypy configuration. This ensures consistency across the team and CI/CD. Pin your mypy version.
1.1. Centralized Configuration
Always configure mypy via pyproject.toml. This is the modern standard.
❌ BAD: mypy.ini or command-line flags only
✅ GOOD: pyproject.toml
# pyproject.toml
[tool.mypy]
python_version = "3.11"
warn_unused_ignores = true
disallow_untyped_defs = true
disallow_incomplete_defs = true
check_untyped_defs = true
no_implicit_optional = true
strict_optional = true
warn_redundant_casts = true
warn_return_any = true
warn_unreachable = true
# Enable strict mode for new code, or specific modules
# strict = true
1.2. CI/CD & Pre-commit Hooks
Integrate mypy into your CI/CD pipeline and as a pre-commit hook. Catch errors early.
# .pre-commit-config.yaml
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.5.0 # Use the latest stable version
hooks:
- id: check-added-large-files
- id: check-yaml
- id: end-of-file-fixer
- id: trailing-whitespace
- repo: https://github.com/pre-commit/mirrors-mypy
rev: v1.8.0 # Pin to the same version as your pyproject.toml
hooks:
- id: mypy
args: [--no-strict-optional, --ignore-missing-imports] # Override global strict for pre-commit if needed
1.3. Incremental Adoption
For existing codebases, start with a minimal configuration and gradually enable stricter checks. Prioritize annotating widely imported modules.
# pyproject.toml (initial, less strict)
[tool.mypy]
python_version = "3.11"
ignore_missing_imports = true # Temporarily ignore missing stubs
allow_untyped_defs = true # Allow unannotated functions
Then, enable stricter checks module by module or globally.
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.
- 2mo ago First seen · 224 lines · 1,436 tokens per session scan A 37599660c1ee
mypy is a cursor rule published in the GitHub repository sanjeed5/awesome-cursor-rules-mdc (3,581 stars, last pushed 5mo ago), licensed CC0-1.0. It adds 1,436 tokens to every session, about $0.0057 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 2026-09-03.
Other cursor rules, from other repositories
python_tests
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python-general-coding-standards
A set of general standards for Python projects, including type hints, input checking, code style, security, web requests, background work, and tests. It also covers FastAPI-specific tasks such as CORS and authentication.
style
Cursor rule "style" from Ayanami1314/swe-pruner, covering style guide, bad, good, test style and bad.
preflight-scripts
Conventions for preflight check scripts.
hatch3r-python-patterns
Python 3.12+ conventions covering uv project management, Ruff lint+format, mypy strict typing, pytest parametrize, and the FastAPI/Django request-path + ORM N+1 floor.
python
Python development guidelines — PEP 8, type hints, error handling, async patterns, packaging, and testing. Applied when working with Python code.