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/pony)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/pony"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/pony.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 | $0.01675 | $0.01675 |
| Opus 5 | $0.00838 | $0.00838 |
| Sonnet 5 | $0.00335 | $0.00335 |
| Haiku 4.5 | $0.00168 | $0.00168 |
Grade A, and why
pony 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 — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pony Best Practices
Pony ORM simplifies database interactions with Pythonic syntax and powerful optimizations. Adhere to these rules for robust, high-performance applications.
1. Always Use db_session for Transaction Management
The db_session context manager is non-negotiable. It ensures proper transaction handling, automatic commits, and rollbacks, preventing resource leaks and data inconsistencies.
❌ BAD: Direct database operations without a session.
from pony.orm import *
from decimal import Decimal
db = Database('sqlite', ':memory:')
class Product(db.Entity):
name = Required(str)
price = Required(Decimal)
db.generate_mapping(create_tables=True)
# This will fail or lead to unexpected behavior in production
p = Product(name='Laptop', price=Decimal('1200.00'))
db.commit() # Manual commit is error-prone and easily forgotten
✅ GOOD: Encapsulate all database work within db_session.
from pony.orm import *
from decimal import Decimal
db = Database('sqlite', ':memory:')
class Product(db.Entity):
name = Required(str)
price = Required(Decimal)
db.generate_mapping(create_tables=True)
@db_session
def create_product(name: str, price: Decimal) -> Product:
p = Product(name=name, price=price)
return p # Auto-committed on exit if no exception
with db_session:
laptop = create_product('Laptop', Decimal('1200.00'))
print(f"Created product: {laptop.name}")
2. Leverage Pythonic Query Syntax for Optimization
Pony translates generator expressions and lambdas into optimized SQL, automatically handling N+1 problems and complex joins. Avoid manual SQL unless absolutely necessary.
❌ BAD: Manual filtering or inefficient data access.
@db_session
def get_expensive_products_bad():
all_products = list(select(p for p in Product)) # Fetches all products
expensive_products = [p for p in all_products if p.price > Decimal('1000')] # Filters in Python
return expensive_products
✅ GOOD: Let Pony generate optimized SQL.
@db_session
def get_expensive_products_good() -> list[Product]:
# Pony translates this directly to SQL: SELECT ... WHERE price > 1000
return select(p for p in Product if p.price > Decimal('1000'))[:] # Use [:] to fetch all results
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 · 229 lines · 1,675 tokens per session scan A fb301610056f
pony 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 1,675 tokens to every session, about $0.0084 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.
Other cursor rules, from other repositories
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
as-contract-cast-smell
// ❌ WRONG — bypasses the family ContractSerializer seam const contract = JSON.parse(raw) as Contract; const contract = JSON.parse(raw) as Contract .
no-backward-compatibility
Do not add backward-compatibility shims or migration scaffolding.
query-optimization
A database performance rule that requires measuring PostgreSQL queries with EXPLAIN ANALYZE under the same user permissions and row-level security (RLS) conditions used in production.
ehs-ims-conventions
EHS IMS app — RBAC, data layer, tRPC, migrations, AI boundaries.
sync-timedb-archive-janitor-contract
Day-close / cold-path worker contracts for synctimedb (A invariants; B tick coordinator retired).