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/scipy)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/scipy"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/scipy.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 | $0.02809 | $0.02809 |
| Opus 5 | $0.01404 | $0.01404 |
| Sonnet 5 | $0.00562 | $0.00562 |
| Haiku 4.5 | $0.00281 | $0.00281 |
Grade A, and why
scipy 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 4d 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 — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SciPy Best Practices
This document outlines the definitive best practices for developing with SciPy. Adhering to these guidelines ensures your code is clean, efficient, and compatible with the broader scientific Python ecosystem.
1. Code Organization and Style
Strict adherence to style guides is non-negotiable for maintainability.
1.1. Line Length and Docstrings
Follow PEP 8 with a strict 88-character line limit. Docstrings must conform to PEP 257 and the NumPy docstring standard, using ASCII characters primarily, with a small whitelist of Unicode symbols.
❌ BAD
def calculate_complex_metric(data_array, method='default', tolerance=1e-6, max_iterations=1000, verbose_output=False):
"""Calculates a very complex metric using various parameters and returns a tuple of results."""
# ... implementation ...
return result1, result2, result3
✅ GOOD
def calculate_complex_metric(
data_array: np.ndarray,
*,
method: str = 'default',
tolerance: float = 1e-6,
max_iterations: int = 1000,
verbose_output: bool = False,
) -> MyResultObject:
"""Calculate a complex metric from `data_array`.
Parameters
----------
data_array : np.ndarray
Input data array.
method : {'default', 'optimized'}, optional
Algorithm method to use. Default is 'default'.
tolerance : float, optional
Convergence tolerance. Default is 1e-6.
max_iterations : int, optional
Maximum number of iterations. Default is 1000.
verbose_output : bool, optional
If True, print detailed progress. Default is False.
Returns
-------
res : MyResultObject
An object with attributes:
statistic : float
The primary calculated statistic.
pvalue : float
The associated p-value.
converged : bool
True if the algorithm converged.
"""
# ... implementation ...
return MyResultObject(statistic=..., pvalue=..., converged=...)
@dataclass
class MyResultObject:
statistic: float
pvalue: float
converged: bool
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.
- 4d ago First seen · 366 lines · 2,809 tokens per session scan A 3870ee5127c1
scipy is a cursor rule published in the GitHub repository sanjeed5/awesome-cursor-rules-mdc (3,571 stars, last pushed 3mo ago), licensed CC0-1.0. It adds 2,809 tokens to every session, about $0.0140 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.
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solver-conventions
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ponytail
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angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
cli-error-handling
CLI command error handling patterns.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.