Create Agent Onboarding

Creating agent onboarding files

Treat the AI as a fresh engineer. Generate a compressed, high-signal AGENTS.md at the repo root - the canonical, tool-agnostic onboarding document read natively by Codex, Copilot, Cursor and imported by Claude Code via a thin CLAUDE.md wrapper - then bootstrap the lifecycle that keeps it current.

Announce at start: "Using create-agent-onboarding to generate this repo's onboarding files."

Runtime awareness

The generation phases below are tool-agnostic and work under Claude Code, Codex, Copilot CLI, or any SKILL.md-compatible agent. Phase 6 installs repo-local hooks only for Claude Code. The Codex plugin supplies optional native hooks after /hooks trust review; copied standalone skills never install Codex settings. When hooks are disabled, unavailable, or untrusted, run update-agent-onboarding manually after meaningful commits.

Phase 0: Pre-flight routing

Inspect the repo root before generating anything:

• AIBOARDING.md exists (legacy v1 layout): stop and run migrate-aiboarding instead - never regenerate from scratch over an existing onboarding investment.
• AGENTS.md already exists: treat it as primary input. Skip greenfield grilling; interrogate only the gaps against the section schema below, then propose a restructure as an approval-gated diff. Never overwrite silently.
• CLAUDE.md already exists: preserve it. The only changes allowed are adding the @AGENTS.md import line and managing an aiboarding-owned block via .aiboarding/tools/inject-fenced (marker-fenced, idempotent, removable).

Shared contracts

AGENTS.md schema - tool-agnostic, no frontmatter, no Claude-specific syntax. H2 sections in this exact order:

1. ## Project Purpose
2. ## Stack and Runtime
3. ## Build, Test, Run - exact commands; fast checks and full checks
4. ## Architecture Map - directories, boundaries, data flow, dependency direction
5. ## Domain Model - entities, workflows, invariants, vocabulary
6. ## Agent Guardrails - what agents must NOT assume/refactor/delete/rename/"simplify"
7. ## Known Failure Modes - mistakes previous agents made or will likely make
8. ## Verification Before Completion - commands agents must run before claiming done
9. ## Escalation - Ask the User When - stop-and-ask cases

Backtick-quote every command, identifier, file path, and error string - the compression byte-preservation checker treats backtick spans as protected.

CLAUDE.md wrapper - first line @AGENTS.md, then an aiboarding-fenced block of Claude-only workflow notes. Never duplicate AGENTS.md content: imports expand into context at launch, so duplication doubles token cost for zero benefit.

.aiboarding/state.json - operational state, one top-level key per line (hooks read it with a line scanner, not a JSON parser):

{
  "aiboarding_version": 2,
  "canonical_file": "AGENTS.md",
  "claude_wrapper": "CLAUDE.md",
  "generated": "YYYY-MM-DD",
  "last_synced_commit": "<git rev-parse HEAD>",
  "last_drift_classification": {},
  "receipts": [
  ]
}

State is committed. Advancing last_synced_commit must never modify AGENTS.md or CLAUDE.md - that separation is what prevents self-referential drift loops.

Phase 1: Background crawl + initial grilling

Run two tracks. A single agent cannot truly act in parallel: perform Track A's file reads first and hold the findings, then immediately open Track B and keep grilling.

Track A - automated discovery (no user input): read dependency manifests (package.json, pyproject.toml, go.mod, Cargo.toml, etc.), the directory structure, CI configs, and any README/docs. Extract tech stack, build/test/run commands, and standard engineering basics. Hold these findings for Phase 3.

Track B - grilling interrogation: open with:

"I'm scanning your codebase structure in the background for the tech stack. While I do that: what is the core business problem this project solves?" Then walk the conceptual tree one question at a time, challenging vague answers and incentivizing a targeted brain-dump per micro-topic. Do not batch questions.

Phase 2: Architectural & AI context

Steer the grilling toward architecture and AI-specific guardrails. Extract constraints and known AI failure modes, e.g.:

"You mentioned a custom Auth provider. What are the architectural gotchas or AI failure modes around it that a future sub-agent must not trip over?" Also cover the two sections agents skip most: what must be verified before claiming work done (Verification Before Completion) and which situations demand stopping to ask the user (Escalation). Continue until you have at least one architectural constraint, one AI-specific failure mode or guardrail, one verification command, and one escalation case.

Phase 3: Reconciliation & gap analysis

HARD GATE - do not start until BOTH Track A (crawl) and Track B (grilling) are complete. Cross-examine Track A findings against Track B answers. Run a short, final grilling pass focused only on discrepancies, e.g.:

"The crawl found a Postgres connection string, but you didn't mention a database. How does Postgres fit the core domain, and are there AI constraints here?"

Phase 4: Synthesis & generation

When the reconciliation pass is complete and no open discrepancies remain, combine verified Track A findings with reconciled Track B domain knowledge. Draft AGENTS.md against the schema above. Nothing Claude-specific goes in it; Claude-only workflow notes belong in the CLAUDE.md wrapper block.

Phase 5: Token compression

Compress the draft by following the compress-onboarding skill: level from config.json (compression_level, default full), high-consequence preservation and any per-region opt-in handled only by that skill, byte-preservation verified with .aiboarding/tools/check-preservation, receipt appended to state.json. Present the compressed document to the user for approval before writing it to the repo root.

Phase 6: Install & bootstrap

After the document is approved and written, install the lifecycle with your own file tools (no shell installer), for cross-platform safety. Every step is idempotent - running create twice must not duplicate hooks, settings entries, or fenced blocks.

1. Locate the templates at <plugin-root>/templates/, where <plugin-root> is two levels up from this skill. Use ${CLAUDE_PLUGIN_ROOT}/templates if set; otherwise resolve relative to this skill's own directory.
2. Write CLAUDE.md: line one @AGENTS.md, then the Claude-notes block via inject-fenced <repo>/CLAUDE.md claude-notes <notes-file>. If CLAUDE.md exists, only add the import line (if absent) and the fenced block.
3. Write config: copy templates/state/config.json to <repo>/.aiboarding/config.json (keep an existing config) and templates/state/dot-gitignore to <repo>/.aiboarding/.gitignore. Defer the initial state.json pointer until the Phase 7 validation record is persisted.
4. Copy hook scripts (Claude Code runtimes only): create <repo>/.aiboarding/hooks/ and copy these six files from <plugin-root>/templates/hooks/ verbatim: run-hook.cmd, _lib, session-start, subagent-start, drift-check, instructions-loaded.
5. Copy tools: create <repo>/.aiboarding/tools/ and copy inject-fenced, check-size-budget, check-preservation, classify-drift, and lifecycle-decision, audit-onboarding-evidence, verify-onboarding-mutations, and write-evidence. Installed tools must byte-match their templates/tools/ sources. Evidence is created lazily; inspect it directly at .aiboarding/evidence/v1/ by matching a record's repository.head, type, and outcome—never add it to state.json or onboarding files.
6. Merge settings (Claude Code runtimes only): merge the hooks block of <plugin-root>/templates/settings/hooks.json into <repo>/.claude/settings.json, per top-level event. Before adding an entry, check for an existing aiboarding entry for that event (a command containing .aiboarding/hooks/run-hook.cmd) and replace it in place. Remove stale entries pointing at the retired pre-task and post-commit hooks, and delete those files from <repo>/.aiboarding/hooks/ if present.

Phase 7: Validation gate (blocking)

Do not report success until every check passes; fix and re-check instead of skipping:

1. AGENTS.md and CLAUDE.md exist; CLAUDE.md contains a line @AGENTS.md.
2. No content duplication: the Claude-notes block must not restate AGENTS.md sections.
3. .aiboarding/tools/check-size-budget AGENTS.md passes as a local sensor (no FAIL; resolve WARNs or get the user's explicit OK), and .aiboarding/tools/audit-onboarding-evidence <repo-root> reports no Codex project-chain failure.
4. Every command quoted in Build, Test, Run and Verification Before Completion resolves against the repo (package scripts, Makefile targets, CI workflows, or a binary on PATH).
5. state.json:last_synced_commit equals git rev-parse HEAD.
6. On Claude Code: the settings merge contains exactly one aiboarding entry per event and no pre-task/post-commit references.

After every required validator passes, write one compact onboarding-validation record with validator identities and repository-relative subjects using .aiboarding/tools/write-evidence; do not retain raw output. Recheck HEAD, then write the initial state.json with that exact last_synced_commit. If validation, evidence writing, or the head recheck fails, do not create or advance canonical state.

Then report which files were created or updated and which hook entries were installed. On Windows without Git Bash, tell the user once: hooks will not fire (run-hook.cmd degrades silently), but native CLAUDE.md/AGENTS.md loading still works.