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/itamarzand88/awesome-agent-conventions/cursorrulesgit clone --depth 1 https://github.com/ItamarZand88/awesome-agent-conventionsWrote 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/itamarzand88/awesome-agent-conventions/cursorrules)<a href="https://agentmods.dev/rules/itamarzand88/awesome-agent-conventions/cursorrules"><img src="https://agentmods.dev/badge/rules/itamarzand88/awesome-agent-conventions/cursorrules.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 | $0.01176 | $0.01176 |
| Opus 5 | $0.00588 | $0.00588 |
| Sonnet 5 | $0.00235 | $0.00235 |
| Haiku 4.5 | $0.00118 | $0.00118 |
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 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.
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.
This is a copy
92% identical to cursorrules — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 105 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.
- 3d ago First seen · 105 lines · 1,176 tokens per session scan A 18d28f18513e
cursorrules is a cursor rule published in the GitHub repository ItamarZand88/awesome-agent-conventions (29 stars, last pushed 1mo ago), licensed MIT. It adds 1,176 tokens to every session, about $0.0059 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to cursorrules, differing in 1 line, and is treated as a copy.
Other cursor rules, from other repositories
git-author
Git author identity for commits so GitHub attributes contributions correctly.
tool-design
Tool design for agents—self-contained tools, minimal sets, token-efficient results, progressive disclosure.
agentic-patterns
Agentic workflow patterns—when to use agents vs workflows, prompt chaining, routing, evaluator-optimizer.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
cli-error-handling
CLI command error handling patterns.