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/Luxvil/ai-coding-rulesWrote 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/luxvil/ai-coding-rules/security)<a href="https://agentmods.dev/rules/luxvil/ai-coding-rules/security"><img src="https://agentmods.dev/badge/rules/luxvil/ai-coding-rules/security.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 | $0.00158 | $0.00158 |
| Opus 5 | $0.00079 | $0.00079 |
| Sonnet 5 | $0.00032 | $0.00032 |
| Haiku 4.5 | $0.00016 | $0.00016 |
Grade A, and why
security 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 today.
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
Windsurf Rules - Security
Apply when working with auth, security, or sensitive code paths.
🔴 Non-Negotiable
- NEVER log tokens, passwords, or API keys
- NEVER commit secrets to version control
- ALWAYS validate and sanitize user input
- ALWAYS use parameterized queries
Authentication & Authorization
- Verify both authn AND authz
- Use constant-time comparison for secrets
- Implement rate limiting on auth endpoints
- Hash passwords with bcrypt/argon2
Session Management
- Use httpOnly, secure, sameSite cookies
- Implement proper session invalidation
- Rotate session IDs after login
API Security
- Validate JWT signatures and expiration
- Set appropriate CORS policies
- Use TLS for all external communication
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.
- today First seen · 30 lines · 158 tokens per session scan A 9ccce10aa0ec
security is a cursor rule published in the GitHub repository Luxvil/ai-coding-rules (3 stars, last pushed today), licensed MIT. It adds 158 tokens to every session, about $0.0008 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 2026-09-08.
Other cursor rules, from other repositories
exam-answer-format
Guidelines for writing exam-style answers in a practical, conversational style with definitions first followed by real-world examples.
lecture-reference-linking
Guidelines for including course materials lists and inline references when writing exam answers or documentation that references course materials.
short-answer-version
Guidelines for creating short/concise versions of detailed answers.
documentation-formatting
Guidelines for formatting markdown documentation to improve readability and scannability.
mermaid-diagrams
Guidelines for adding Mermaid diagrams to exam answers and documentation with automatic SVG generation support.
human-writing-style
Rules for writing naturally and authentically - avoid robotic AI tone in documentation, code comments, error messages, and explanations. Write like a human colleague, not a customer service bot.