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/pillow)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/pillow"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/pillow.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 | $0.01632 | $0.01632 |
| Opus 5 | $0.00816 | $0.00816 |
| Sonnet 5 | $0.00326 | $0.00326 |
| Haiku 4.5 | $0.00163 | $0.00163 |
Grade A, and why
pillow 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pillow Best Practices
Pillow is the definitive image processing library for Python. Adhere to these guidelines to ensure your image manipulation code is robust, performant, and maintainable.
Critical Guidelines:
1. Resource Management: Always Use Context Managers
Images opened from files consume system resources. Ensure they are properly closed using with statements.
❌ BAD:
from PIL import Image
im = Image.open("input.jpg")
# ... process im ...
# Resource might leak if not explicitly closed or on error
# im.close() # Often forgotten, especially on exceptions
✅ GOOD:
from PIL import Image
def process_image_with_thumbnail(path: str) -> None:
with Image.open(path) as im:
# Image is automatically closed when exiting the 'with' block
im.thumbnail((128, 128))
im.save("thumbnail.jpg")
2. Prefer High-Level API for Common Operations
Pillow provides a rich, high-level API for most common image transformations. Use these methods instead of manual pixel manipulation or low-level ImageFile operations.
❌ BAD:
from PIL import Image
# Avoid manual pixel access for transformations; it's inefficient and error-prone
def resize_manual(im: Image.Image, size: tuple[int, int]) -> Image.Image:
new_im = Image.new(im.mode, size)
# ... complex, slow loop to copy pixels ...
return new_im
✅ GOOD:
from PIL import Image, ImageFilter
def apply_common_transforms(im: Image.Image) -> Image.Image:
# Use built-in methods for efficiency and correctness
im_resized = im.resize((256, 256), Image.Resampling.LANCZOS)
im_rotated = im_resized.rotate(45, expand=True)
im_blurred = im_rotated.filter(ImageFilter.BLUR)
return im_blurred
3. Explicitly Import Custom Plugins
Pillow no longer auto-loads plugins (since 2.1.0). If you're using a custom image format or a third-party plugin, you must import it explicitly before use.
❌ BAD:
from PIL import Image
# Assuming 'MyCustomImagePlugin.py' is on PYTHONPATH
# This will fail if MyCustomImagePlugin is not explicitly imported
try:
with Image.open("custom.myformat") as im:
im.save("output.png")
except Exception as e:
print(f"Error: {e}") # Likely 'cannot identify image file'
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 · 209 lines · 1,632 tokens per session scan A 0542bab2af34
pillow 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 1,632 tokens to every session, about $0.0082 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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