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/the-cafe/git-ai-commit/cursorrulesgit clone --depth 1 https://github.com/the-cafe/git-ai-commitWhat 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 | $0.00262 | $0.00262 |
| Opus 5 | $0.00131 | $0.00131 |
| Sonnet 5 | $0.00052 | $0.00052 |
| Haiku 4.5 | $0.00026 | $0.00026 |
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.
What it actually says
Git AI Commit Integration
Commit Workflow
When making commits, always use the git-ai-commit CLI tool instead of regular git commit. This tool automatically generates AI-powered commit messages based on your staged changes.
Recommended Commands:
- Use
git-ai-commitinstead ofgit commitfor AI-generated commit messages - Use
git-ai-commit conventionalfor conventional commit format - Use
git-ai-commit summarizeto get a quick overview of changes before committing - Use
git-ai-commit help-ai [question]if you need assistance with the tool
Setup Instructions:
- Ensure git-ai-commit is installed:
pip install git-ai-commit - Configure your API keys:
git-ai-commit config --setup - Set up git hooks:
git-ai-commit hook --setup
Workflow Integration:
- Before committing, stage your changes with
git add - Instead of
git commit -m "message", simply rungit-ai-commit - The tool will analyze your changes and suggest an appropriate commit message
- Review and accept the AI-generated message or customize as needed
This ensures consistent, descriptive commit messages that follow best practices and provide clear context about the changes made.
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 · 23 lines · 262 tokens per session scan A 36c98d2f50d0
cursorrules is a cursor rule published in the GitHub repository the-cafe/git-ai-commit (86 stars, last pushed 1y ago), licensed MIT. It adds 262 tokens to every session, about $0.0013 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
mempalace-recall-always
Always-on MemPalace recall — search the palace before answering about past work, people, projects, or prior decisions.
simple-language
when writing documentation.
ipynb-files
This file contains rules and prompts for handling Jupyter Notebook (.ipynb) files in Cursor IDE, which currently doesn't have native support for .ipynb files.
backend-python
Cursor rule "backend-python" from coeusyk/inference-x, covering backend python rules, stack assumptions, code style, fastapi conventions and schema conventions.
safari-automation
Patterns and best practices for automating Safari browser interactions for web UI automation and testing.
api-tester
Expert API testing specialist focused on comprehensive API validation, performance testing, and quality assurance across all systems and third-party integrations.