Onboard Your Agent
One paste that feeds any coding agent the whole playbook and has it audit your repo — Claude Code, Cursor, Windsurf, Codex, Copilot, and more.
Onboard Your Agent
One paste that feeds any coding agent the whole playbook and has it audit your repo — Claude Code, Cursor, Windsurf, Codex, Copilot, and more.
The idea
The playbook is agent-agnostic. It is plain Markdown, served both as a human site and as machine-readable bundles. Any agent that can fetch a URL (or that you can paste text into) can ingest the whole thing, compare it against your repository, and tell you what to adopt — a facilitated training pass, not a manual read-through.
Two steps: point the agent at the bundle, then ask it to audit your repo against it.
The universal onboarding prompt
Paste this into your agent of choice. It works unchanged across tools — it only asks the agent to fetch, audit, and plan, never to edit blindly.
You are onboarding to a shared engineering playbook for shipping production
software with AI coding agents.
1. Fetch and read the full playbook bundle:
https://playbook.agentskit.io/llms-full.txt
(Site map: https://playbook.agentskit.io/llms.txt — fetch individual docs
from the /raw/ paths if you can't load the whole bundle at once.)
2. Then audit THIS repository against it:
- Which playbook practices already hold here?
- Which are missing or violated, ranked by risk
(security > correctness > quality > governance > DX)?
- Which are not applicable to this stack, and why?
3. Propose a short, prioritized adoption plan: the 5 highest-leverage changes
for this repo, each with the playbook doc it comes from and a concrete first
step.
4. Draft (or update) the repo's bootstrap doc — CLAUDE.md, AGENTS.md,
.cursor/rules, .windsurfrules, or .github/copilot-instructions.md as
appropriate for the agent in use — using the playbook's template as the
starting point.
Do not change code yet. Output the audit and the plan first, then wait for my
go-ahead.Why "don't change code yet". The first pass is an audit. Letting the agent rewrite the repo before you've read its plan is how you get a 40-file diff nobody asked for. See
pillars/ai-collaboration/human-in-the-loop-pattern.md.
Where the playbook lives (machine-readable)
| Endpoint | What it is | Use it for |
|---|---|---|
/llms-full.txt | Every doc concatenated into one file | One-shot context load / RAG indexing |
/llms.txt | Site map of all docs with links | Letting the agent pick which docs to fetch |
/raw/<path>.md | Raw Markdown for any single doc | Targeted reads (e.g. /raw/pillars/security/rbac-pattern.md) |
/playbook-bundle.zip | Zip of all docs | Local indexing / offline RAG |
Per-tool setup
Every agent reads a bootstrap doc first. Adopt the playbook by putting your repo's rules in the file your agent already looks for — see pillars/ai-collaboration/agent-compatibility-pattern.md for the full mapping.
Claude Code
Paste the prompt in a session. Claude Code fetches the bundle and reads your repo directly. Persist the result to CLAUDE.md (or AGENTS.md) at the repo root so every future session starts pre-trained.
Cursor
Paste the prompt into chat (Agent mode so it can read the repo). Save the adopted rules to .cursor/rules/ (one .mdc file per concern) or a root AGENTS.md — Cursor reads both.
Windsurf
Paste into Cascade. Save the rules to .windsurfrules at the repo root.
GitHub Copilot
Paste into Copilot Chat. Persist repo-wide rules to .github/copilot-instructions.md.
OpenAI Codex / Codex CLI
Paste into the Codex prompt. Codex reads AGENTS.md at the repo root — write the adopted rules there.
Aider, Cline, Zed, and others
Any agent that accepts a system/context file works the same way: paste the prompt, then save the adopted rules into that tool's convention file (e.g. Aider's CONVENTIONS.md). When in doubt, a root AGENTS.md is the most widely-read fallback.
After the audit
- Read the agent's prioritized plan. Push back on anything that doesn't fit your stack.
- Commit the bootstrap doc first — it's the durable artifact that trains every future session.
- Adopt the quality gates early; they catch regressions the moment the agent starts writing code.
- Re-run the onboarding prompt after major dependency or architecture changes — the applicable subset of the playbook shifts as the repo grows.
See also
pillars/ai-collaboration/agent-compatibility-pattern.md— the bootstrap-doc-per-tool mapping.pillars/ai-collaboration/bootstrap-doc-pattern.md— what goes in the bootstrap doc.templates/CLAUDE.md.template.md— the starting skeleton (works as AGENTS.md too).getting-started.mdx— adopt the playbook step by step.