Train coding agents to ship code your team would merge.
Open rules, prompts, memory, evals, and executable gates turn every correction into behavior your coding agents can repeat.
Train the behavior, not the model.
Part of the AgentsKit ecosystem: build on the AgentsKit foundation, start from the Registry, ship with the Playbook, and operate on AKOS. Also: Chat and Doc Bridge.
# CLAUDE.md ## Non-negotiables 1. No `any`. Schema parse every boundary. 2. Named exports only. 3. Typed AppError + stable codes. 4. createLogger(tag); no console.log. 5. ADR before architecture. RFC before breaking contracts. 6. Ship complete or don't ship. 7. Merges sum work, never subtract. 8. Tokens, intl, primitives — no raw values on user-facing surfaces. ## Before you ship ``` pnpm check:quality-gates pnpm check:all ```
One open harness. Every coding agent.
Claude Code, Codex, Cursor, Copilot, and other agents learn the same repository behavior without changing the underlying model.
Sized files, named exports, no nested-ternary soup — diffs a human can actually read, understand, and approve without rubber-stamping.
Patterns and gates for secrets, auth boundaries, and safe defaults — security designed into the contract, not bolted on after a leak. Production runtime enforcement lives in AKOS when you need it.
Tests in the diff, gates green, complete-or-it-doesn't-ship — no half-built screens marked done, no stale branches grinding against main.
Turn every failure into reusable training.
The harness captures production corrections as rules, context, and gates that the next coding-agent session can apply automatically.
Reinvented primitives
Agent rewrites a helper that already exists upstream — instead of depending on it.
Unreviewable diffs
Ternaries nested until no human can read the file, and the PR rubber-stamps through.
Deleted peer work
A merge resolved with checkout --theirs silently wipes another author's code.
Fake “done”
Screen marked shipped while half its tabs still throw not implemented.
Session amnesia
Agent repeats a mistake you already fixed last week — context lost between runs.
Stale-branch grind
Agent keeps building on a branch while main has already gone red.
Behavior you can inspect, adapt, and enforce.
Production-earned
Every rule traces to a reproducible failure mode. No theory; only patterns paid for in shipped repos.
Dual-mode docs
Each page has a Human TL;DR and a For-Agents section. Optimised for both linear reading and RAG retrieval.
Gates included
13 reference gate scripts (Node 22, zero deps) you can drop into any repo. Pure copy-paste.
Drop-in templates
ADR, RFC, PR-intent, CLAUDE.md, AGENTS.md, MEMORY.md — ready for any project.
Six pillars. Six SDLC phases. One matrix.
Each pillar carries a universal (stack-agnostic) layer plus concrete recipes for a TypeScript stack. Together they form a checklist for any agent-augmented team.
Architecture
Modular boundaries, typed contracts, ADRs / RFCs, file-size budgets, anti-overengineering, distributed data, event streaming, multi-region.
Security
RBAC, vault, audit ledger, egress allowlist, vulnerability mgmt, secrets deep, multi-tenant isolation, compliance (SOC 2 / GDPR), AI/LLM safety.
UI / UX
Design tokens, primitives catalog, intl + ICU, empty states, a11y (WCAG-AA deep), motion + reduced-motion, whitelabel, design-system governance.
Quality
Test pyramid, gates, sanity, mutation, observability + SLOs, performance budgets, chaos engineering, CI/CD pipeline, FinOps, contract testing.
Governance
PR intent manifest, merge rules, tombstones, phased PRs — keeps multi-author work additive instead of subtractive.
AI Collaboration
Bootstrap docs (CLAUDE.md / AGENTS.md), persistent memory, sub-agent recipes, slash commands, concurrent-agent survival.
Built so your agents can find what they need.
- 1
/llms.txtat root — convention for LLM-readable site map. - 2Each doc serves a raw
.mdalongside the HTML — agents fetch source. - 3Structured data (JSON-LD) and OpenGraph on every page.
- 4Single-file
llms-full.txtbundles every doc for one-shot RAG indexing. - 5Zip bundle (
/playbook-bundle.zip) for download + local indexing.
curl playbook.agentskit.io/raw/pillars/security/rbac-pattern.mdcurl playbook.agentskit.io/llms-full.txtcurl -O playbook.agentskit.io/playbook-bundle.zipInstall the harness in one paste.
Paste this into any coding agent. It loads the open playbook, audits your repository, and proposes a prioritized adoption plan before touching code. You train repeatable behavior, not model weights.
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 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.
The AgentsKit ecosystem
Train the agent. Reuse the behavior everywhere.
Playbook is the open engineering harness inside one connected toolkit, from ready-made source to governed production.
Your next agent session should start better than the last one.
Adopt the open harness, persist your engineering standards, and make every correction reusable.
Free and open (CC-BY-4.0). If the playbook saved you a code review, drop a star ↗ — it helps other teams find it.
Want the platform these practices run on? Explore AgentsKit ↗