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/requests)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/requests"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/requests.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 | $0.02613 | $0.02613 |
| Opus 5 | $0.01307 | $0.01307 |
| Sonnet 5 | $0.00523 | $0.00523 |
| Haiku 4.5 | $0.00261 | $0.00261 |
Grade A, and why
requests scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response1 = requests.get("https://api.example.com/data/1") How it starts
The opening of the file, as written. The whole thing — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
requests Best Practices
requests is the definitive HTTP client for Python. To build robust, performant, and maintainable web interactions, you must leverage its advanced features and integrate them with modern Python best practices. This guide provides actionable rules for our team.
1. Always Use Session Objects
For any code making more than a single HTTP call, or any reusable API client, you must use a requests.Session object. Sessions provide connection pooling and cookie persistence, drastically improving performance and resource usage.
❌ BAD: Direct requests calls
import requests
# Each call creates a new connection
response1 = requests.get("https://api.example.com/data/1")
response2 = requests.get("https://api.example.com/data/2")
✅ GOOD: Use a Session with a context manager
import requests
with requests.Session() as session:
# Connections are pooled and reused
response1 = session.get("https://api.example.com/data/1")
response2 = session.get("https://api.example.com/data/2")
2. Configure Retries and Timeouts with HTTPAdapter
Enhance session reliability by mounting a custom HTTPAdapter to handle retries with backoff and set default timeouts. This prevents flaky network issues from crashing your application and ensures requests don't hang indefinitely.
❌ BAD: No retries, no default timeouts
import requests
with requests.Session() as session:
# Will fail on first network glitch, can hang forever
response = session.get("https://api.example.com/flaky-endpoint")
✅ GOOD: Mount an HTTPAdapter with Retry logic
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
# Configure retry strategy
retry_strategy = Retry(
total=3, # Total number of retries
backoff_factor=1, # Exponential backoff (1, 2, 4 seconds)
status_forcelist=[429, 500, 502, 503, 504], # HTTP statuses to retry on
allowed_methods=["HEAD", "GET", "OPTIONS"] # Methods to retry
)
adapter = HTTPAdapter(max_retries=retry_strategy)
with requests.Session() as session:
session.mount("http://", adapter)
session.mount("https://", adapter)
# All requests made with this session will use the retry logic
response = session.get("https://api.example.com/flaky-endpoint", timeout=(5, 10))
Note: timeout should still be explicitly set on individual requests, even with an adapter, to override or confirm the default.
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 · 310 lines · 2,613 tokens per session scan A a9febc94b4e9
requests 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 2,613 tokens to every session, about $0.0131 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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Python best practices and patterns for modern software development with Flask and SQLite.
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env-validation-gate
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