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.
npx agentmods add rules/grapeot/devin.cursorrules/cursorrulesgit clone --depth 1 https://github.com/grapeot/devin.cursorrulesWhat 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 | $0.01149 | $0.01149 |
| Opus 5 | $0.00575 | $0.00575 |
| Sonnet 5 | $0.00230 | $0.00230 |
| Haiku 4.5 | $0.00115 | $0.00115 |
Grade A, and why
cursorrules 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 yesterday.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cursorrules — 92% identical, 1 lines differ
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
During your interaction with the user, if you find anything reusable in this project (e.g. version of a library, model name), especially about a fix to a mistake you made or a correction you received, you should take note in the Lessons section in the .cursorrules file so you will not make the same mistake again.
You should also use the .cursorrules file as a Scratchpad to organize your thoughts. Especially when you receive a new task, you should first review the content of the Scratchpad, clear old different task if necessary, first explain the task, and plan the steps you need to take to complete the task. You can use todo markers to indicate the progress, e.g.
[X] Task 1
[ ] Task 2
Also update the progress of the task in the Scratchpad when you finish a subtask. Especially when you finished a milestone, it will help to improve your depth of task accomplishment to use the Scratchpad to reflect and plan. The goal is to help you maintain a big picture as well as the progress of the task. Always refer to the Scratchpad when you plan the next step.
Tools
Note all the tools are in python3. So in the case you need to do batch processing, you can always consult the python files and write your own script.
Screenshot Verification
The screenshot verification workflow allows you to capture screenshots of web pages and verify their appearance using LLMs. The following tools are available:
- Screenshot Capture:
venv/bin/python3 tools/screenshot_utils.py URL [--output OUTPUT] [--width WIDTH] [--height HEIGHT]
- LLM Verification with Images:
venv/bin/python3 tools/llm_api.py --prompt "Your verification question" --provider {openai|anthropic} --image path/to/screenshot.png
Example workflow:
from screenshot_utils import take_screenshot_sync
from llm_api import query_llm
# Take a screenshot
screenshot_path = take_screenshot_sync('https://example.com', 'screenshot.png')
# Verify with LLM
response = query_llm(
"What is the background color and title of this webpage?",
provider="openai", # or "anthropic"
image_path=screenshot_path
)
print(response)
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.
- yesterday First seen · 104 lines · 1,149 tokens per session scan A f90a8a4723ba
cursorrules is a cursor rule published in the GitHub repository grapeot/devin.cursorrules (5,970 stars, last pushed 1y ago), licensed MIT. It adds 1,149 tokens to every session, about $0.0057 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 2026-08-30.
Other cursor rules, from other repositories
050-plan
When the user types /plan or asks to create a project plan, feature PRD, or retrospective.
dreamd-recall
Recall lessons, decisions, and prior context from the .agent/ memory daemon. Use when starting work in a project that has a .agent/ folder, when the user references a past decision, or when you are about to make a choice that has a documented prior.
session-memory
Use at conversation wrap-up or when the user explicitly indicates end-of-session — capture residual lessons not captured in-flight.
context-recorder-system
Context Recorder System (记录员系统) - 模块化索引文件.
rules
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
misc-documenting-learnings-and-clarifying-assumptions
Documenting Learnings and Clarifying Assumptions for Efficient Task Execution.