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/vanessamarely/ai-playbook-repositoWrote 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/commands/vanessamarely/ai-playbook-reposito/ai-tool-setup)<a href="https://agentmods.dev/commands/vanessamarely/ai-playbook-reposito/ai-tool-setup"><img src="https://agentmods.dev/badge/commands/vanessamarely/ai-playbook-reposito/ai-tool-setup/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/commands/vanessamarely/ai-playbook-reposito/ai-tool-setup"><img src="https://agentmods.dev/badge/commands/vanessamarely/ai-playbook-reposito/ai-tool-setup.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.01379 |
| Opus 5 | $0.00000 | $0.00690 |
| Sonnet 5 | $0.00000 | $0.00276 |
| Haiku 4.5 | $0.00000 | $0.00138 |
Grade A, and why
ai-tool-setup 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Tool Setup
Generate or update AI assistant instruction files for all four supported tools — GitHub Copilot, Claude Code, Cursor, and OpenAI Codex CLI — so they share a consistent set of policies derived from this playbook. Use when onboarding a new AI tool or refreshing existing configuration after playbook changes. Not for writing new rules/commands (/skill-creator), building application code, or general documentation.
Inputs
targetTool: one ofcopilot,claude,cursor,codex, orall.projectRoot: absolute path to the repository root.playbookRoot: path to the playbook directory (default:ai-playbook).
Outputs
Each tool has its own real, native structure — there is no shared folder all four read:
| Tool | Always-loaded file | Modular / additional files |
|---|---|---|
| GitHub Copilot | .github/copilot-instructions.md |
.github/instructions/*.instructions.md (each with applyTo frontmatter globs) |
| Claude Code | CLAUDE.md (repo root) |
.claude/agents/, .claude/skills/, .claude/commands/ as needed |
| Cursor | none required | .cursor/rules/*.mdc (alwaysApply: true for always-on, globs for file-type triggers, description-only for Agent Requested) and .cursor/commands/*.md for explicit /name invocation |
| OpenAI Codex CLI | AGENTS.md (nearest wins for nested overrides) |
.agents/skills/<name>/SKILL.md for bundled procedures |
Procedure
- Validate inputs — confirm
projectRootexists and looks like a real project (package.json,pom.xml, orpyproject.toml); confirmplaybookRootcontainspolicies/(and, in this reference repo,.claude/agents/and.claude/skills/as the canonical source material). If either check fails, report the error and stop. - Detect the project type — inspect
projectRootforpackage.json(+React/Express/Nest/Fastify deps),pom.xml/build.gradle,pyproject.toml/setup.py/requirements.txt. ExtractprojectType,framework,language. - Load applicable policies from
<playbookRoot>/policies/: always readworkspace-policy.mdandstyle-output.md; readfrontend-policy.mdif the project is frontend,backend-policy.mdif it's backend (both if full-stack). - Build the routing table mapping project type to relevant procedures:
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 · 60 lines · 0 tokens per session scan A c367527d3323
ai-tool-setup is a command published in the GitHub repository vanessamarely/ai-playbook-reposito (2 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,379 tokens. 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 2026-08-31.
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clarify
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specify
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analyze
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converge
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implement
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