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/hypothesis)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/hypothesis"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/hypothesis/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/hypothesis"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/hypothesis.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 | $0.02148 | $0.02148 |
| Opus 5.5 | $0.00859 | $0.00859 |
| Sonnet 5.5 | $0.00430 | $0.00430 |
| Haiku 4.5 | $0.00215 | $0.00215 |
Grade A, and why
hypothesis 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
hypothesis Best Practices
Hypothesis is a powerful property-based testing library. These guidelines ensure your Hypothesis tests are effective, maintainable, and integrate seamlessly into modern Python development workflows.
1. Code Organization and Structure
Always integrate Hypothesis with pytest. Keep test files in a dedicated tests/ directory, mirroring your src/ structure. Adhere to PEP 8, use clear docstrings, and apply type hints.
❌ BAD: Generic test function names, no st alias, mixed with application code.
# my_app/utils.py
from hypothesis import given, strategies
def add(a, b): return a + b
@given(strategies.integers(), strategies.integers())
def test_add(a, b):
assert add(a, b) == a + b
✅ GOOD: Dedicated test files, st alias, clear naming, pytest integration.
# src/my_app/utils.py
def add(a: int, b: int) -> int:
"""Adds two numbers."""
return a + b
# tests/test_utils.py
from hypothesis import given, strategies as st
import pytest
from src.my_app.utils import add
@given(st.integers(), st.integers())
def test_add_is_commutative(a: int, b: int) -> None:
"""Verify addition is commutative."""
assert add(a, b) == add(b, a)
@given(st.integers())
def test_add_identity_element(a: int) -> None:
"""Verify zero is the identity element for addition."""
assert add(a, 0) == a
2. Strategy Selection and Constraints
Always use the most specific and constrained strategies possible. This improves performance and focuses generated examples on relevant edge cases. Use st.composite for dependent generation.
❌ BAD: Overly broad strategies or excessive filtering with assume() for basic constraints.
@given(st.integers(), st.integers())
def test_division(numerator, denominator):
assume(denominator != 0) # Discards many examples
assume(numerator % denominator == 0) # Discards even more
assert numerator / denominator == numerator // denominator
✅ GOOD: Constrain strategies directly. Use st.composite for dependent values.
from hypothesis import given, strategies as st, assume
from hypothesis.extra.math import non_zero_floats
from typing import Tuple
@given(st.integers(), st.integers(min_value=1, max_value=100))
def test_division_positive_divisor(numerator: int, divisor: int) -> None:
"""Test integer division with a positive divisor."""
assert (numerator // divisor) * divisor + (numerator % divisor) == numerator
@st.composite
def non_zero_pairs(draw) -> Tuple[int, int]:
"""Generates a pair of integers where the second is non-zero."""
numerator = draw(st.integers())
denominator = draw(st.integers().filter(lambda d: d != 0))
return numerator, denominator
@given(non_zero_pairs())
def test_division_any_non_zero(pair: Tuple[int, int]) -> None:
"""Test division property with any non-zero denominator."""
numerator, denominator = pair
# Property: (q * d) + r = n, where 0 <= abs(r) < abs(d)
quotient = numerator // denominator
remainder = numerator % denominator
assert (quotient * denominator) + remainder == numerator
assert abs(remainder) < abs(denominator)
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 · 251 lines · 2,148 tokens per session scan A 4d29eaefdd77
hypothesis 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 2,148 tokens to every session, about $0.0086 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
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-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.