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 skills add divar-ir/ai-doc-gen --skill generate-ai-rulesgit clone --depth 1 https://github.com/divar-ir/ai-doc-genWrote 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/skills/divar-ir/ai-doc-gen/generate-ai-rules)<a href="https://agentmods.dev/skills/divar-ir/ai-doc-gen/generate-ai-rules"><img src="https://agentmods.dev/badge/skills/divar-ir/ai-doc-gen/generate-ai-rules/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/divar-ir/ai-doc-gen/generate-ai-rules"><img src="https://agentmods.dev/badge/skills/divar-ir/ai-doc-gen/generate-ai-rules.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00091 | $0.01109 |
| Opus 5 | $0.00046 | $0.00554 |
| Sonnet 5 | $0.00018 | $0.00222 |
| Haiku 4.5 | $0.00009 | $0.00111 |
Grade A, and why
generate-ai-rules 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 10d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate AI Rules
Generate configuration files that help AI coding assistants work effectively with a codebase. Three targets, generated from the same analysis so they stay consistent:
- AGENTS.md — the cross-tool standard (agents.md), read by most AI coding tools including Claude Code, Cursor, Codex, and Gemini CLI.
- CLAUDE.md — Claude Code's project instructions file.
- .cursor/rules/*.mdc — Cursor's scoped project rules.
Workflow
1. Determine targets and gather data
- Generate all three targets by default; the user may skip any (e.g., "skip cursor rules", "keep my existing CLAUDE.md").
- If a target file already exists and the user didn't say to regenerate it, ask whether to update it or leave it alone.
- Check
<repo>/.ai/docs/for analysis documents from theanalyze-codebaseskill. If present, use them as the primary source (spot-check against the code — they may be stale). If absent, offer to runanalyze-codebasefirst, or explore the codebase directly for a quicker pass.
2. Generate the files
Shared principles for all targets:
- Accuracy: every command, path, and convention must come from the actual project. Test that commands at least look right against the manifest files (e.g., scripts in package.json, tasks in Makefile,
uv runvspip). - Actionability: specific, executable instructions beat vague guidance. "Run
uv run ruff format src/" beats "format your code". - Conciseness: these files are loaded into every AI session — every line costs context. Only include what the AI cannot cheaply discover by reading the code: commands, non-obvious conventions, gotchas, things that have gone wrong before. Do not restate what the code structure makes obvious.
- Consistency: same terminology and architecture descriptions across all generated files.
AGENTS.md
The primary file — write it first, and write it best. Target well under 150 lines.
- Project overview (1–2 sentences)
- Build, test, run, lint commands (in backticks, copy-pasteable)
- Architecture overview (3–5 bullets)
- Code style conventions
- Testing instructions
- Git workflow (commit format, PR process)
- Key project-specific conventions and gotchas
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.
- 10d ago First seen · 84 lines · 91 tokens per session scan A 49db329175da
generate-ai-rules is a skill published in the GitHub repository divar-ir/ai-doc-gen (755 stars, last pushed 1mo ago), licensed MIT. It adds 91 tokens to every session and 1,109 once invoked, about $0.0005 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.
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