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/huggingface)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/huggingface"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/huggingface.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 | $0.02135 | $0.02135 |
| Opus 5 | $0.01068 | $0.01068 |
| Sonnet 5 | $0.00427 | $0.00427 |
| Haiku 4.5 | $0.00214 | $0.00214 |
Grade A, and why
huggingface 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 — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hugging Face Best Practices
This guide establishes the definitive coding standards for developing with Hugging Face libraries, particularly transformers, and interacting with the Hugging Face Hub. Adherence ensures high-quality, performant, and maintainable ML code.
1. Code Organization and Structure
Always structure your Hugging Face projects for clarity, modularity, and Hub compatibility.
1.1 Model and Tokenizer Loading
Use Auto classes and from_pretrained for standard model and tokenizer loading. This ensures compatibility and leverages the Hub's versioning.
❌ BAD (Hardcoding specific model classes, limits flexibility)
from transformers import BertForSequenceClassification, BertTokenizer
model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
✅ GOOD (Flexible, auto-detecting model types and versions)
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
1.2 Project Structure for Hub Repositories
Embrace the "Git-first" workflow. Every model, dataset, or Space lives in a Git repository on the Hub. Structure your local project to mirror this, ensuring README.md (Model Card) and .gitattributes are present.
✅ GOOD (Standard Hub repository structure)
my-awesome-model/
├── src/
│ ├── model.py # Custom model definition (if any)
│ └── utils.py # Helper functions
├── scripts/
│ └── train.py # Training script
├── data/ # Small datasets, or pointers to Hub datasets
├── config.json # Model configuration
├── tokenizer.json # Tokenizer configuration
├── model.safetensors # Model weights (or .bin)
├── README.md # Crucial Model Card with metadata
├── .gitattributes # For large file handling (Xet)
└── requirements.txt # Project dependencies
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 · 245 lines · 2,135 tokens per session scan A 23ebcbd12801
huggingface 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 2,135 tokens to every session, about $0.0107 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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