docs-guide/features/ai-features.mdx.
Registry
The AI-tools registry is the canonical source of truth for everything underai-tools/**. It records what exists, how each artifact is classified, which lane it belongs to, and its lifecycle state.
Validator commands
Registry schema summary
The registry schema (ai-tools-registry.schema.json) requires five top-level keys:
Each artifact entry records:
id, path, artifact_type, category, lifecycle_state, current_lane, target_lane, canonical_source, derived_outputs, runtime_targets, validators, repair_commands, catalog_group, status, migration_wave, and notes.
Lane model
The registry organises all AI-tools artefacts into seven lanes. Lane assignment is enforced by path pattern matching in the registry schema.Root Governance Status
ai-tools/ is an approved root subsystem in the current root-governance contract.
Current implementation rule:
- keep
ai-tools/at repo root - govern it explicitly instead of treating it as tolerated drift
- keep generated outputs derived only
- defer any structural review of whether it should move to a later governance pass
workspace/plan/active/AI-TOOLS-GOVERNANCE/structure.mdworkspace/plan/active/AI-TOOLS-GOVERNANCE/architecture-audit.md
Shared classification model
The governed skill system now uses a script-like taxonomy plus AI-specific layers:metadata.category remains in place as a compatibility field while the current skill/export stack still depends on it.
Lifecycle states
Every artifact in the registry carries one of these lifecycle states:Adapter inventory
All active native adapter files and their owning tools. Each adapter is a thin pointer back toAGENTS.md; policy must not drift between surfaces. Canonical governance policy: docs-guide/policies/agent-governance-framework.mdx.
Canonical adapter files
Every file listed below has been verified on disk.
Legacy assistant redirects may still exist outside the active adapter set, but they are not part of the governed root contract.
Supplemental cross-agent packs (generated, not canonical)
Shared adapter rules
All active agent-governance surfaces enforce:- Agents must not use port
3000for local Mintlify, preview, or browser-validation sessions. - Choose a non-3000 port explicitly when running local preview commands.
Adapter consumption model
The adapter rule is now explicit:- canonical logic should exist once
- agent wrappers may exist many times
- generated outputs should exist zero times by hand
- register or expose canonical repo capabilities in the native interface of the agent
- add agent-specific invocation or runtime glue only where required
- avoid owning unique workflow logic, policy, or taxonomy definitions
.claude/skills/*may stay for Claude discovery, but should be thin wrappers only.github/AGENTS.mdremains a Codex runtime extension layer, not a parallel skill catalogue- generated packs under
ai-tools/agent-packs/**are onboarding/reference outputs, not canonical adapter sources
Skills governance
Portable skills are the primary unit of reusable AI capability in this repository. They follow a template-based authoring model with deterministic sync to local and external targets.Template source
Canonical source:ai-tools/ai-skills/templates/*.template.md
The templates directory currently contains 53 template files (.template.md, live count 2026-05-25) covering bootstrap, authoring, auditing, validation, generation, and research workflows. Several templates include companion reference bundles stored in sibling directories named <template-stem>.references/.
Canonical versus adapter versus generated
Use this boundary when deciding where a capability belongs:
Repo-governed logic should not live only in generated outputs or only in a user-local install target such as
~/.codex/skills.
Dispatcher/orchestrator model
Dispatchers are first-class governed assets for repeatable delivery workflows. They orchestrate atomic skills but do not replace them. Phase 1 dispatcher family:research-review-packetreview-fixhandover-readinesspage-shiprepo-cleanup-handover
Skill lifecycle
- Author a template in
ai-tools/ai-skills/templates/following the naming conventionNN-skill-name.template.md. - Register the new artifact in
ai-tools/registry/ai-tools-registry.jsonwith correct lane (templates), lifecycle state (canonical-template), and metadata. - Sync to local skill roots via the sync mechanism (see below).
- Export to agent packs via the cross-agent packager.
- Validate with the registry validator to confirm coverage and lane alignment.
Sync mechanism
Thesync-codex-skills.js script handles safe upsert of templates into Codex-compatible skill directories.
Runbook:
agents/openai.yamlis generated deterministically.- Optional companion bundles are sourced from directories beside the template file:
<template-stem>.references/syncs toreferences/<template-stem>.scripts/syncs toscripts/<template-stem>.assets/syncs toassets/
- Sync is managed, not destructive: managed files are created or updated from the canonical template bundle; stale managed bundle files are pruned on resync; unmanaged skills and unmanaged files inside a skill folder are preserved.
Local Codex boundary
~/.codex/skills must be treated as two distinct buckets:
- repo-managed skills synced from this repo’s canonical templates
- global/plugin/system skills that are not part of repo governance
Subsystem-scoped audit pipeline catalog
These files remain active for the repo-audit pipeline but are not the full AI-tools registry:
Use these files only for repo-audit orchestrator run order, cross-agent packager manifest inputs, and audit-pipeline policy docs that describe that subsystem specifically.
Rules governance
Theai-tools/ai-rules/ directory contains legacy and imported AI rule material. This lane is classified as advisory_only in the registry and is pending later normalisation.
Current contents
Retired rules
The_retired/ subdirectory contains six retired files: .augment-guidelines, .AI-SAFEGUARDS.md, REVIEW_TABLE.md, AI_GUIDELINES.md, tasks-directory-structure.mdc, UNIVERSAL-AI-PROTOCOL.md, and imported-copilot-instructions.md. These are not active governance sources.
Relationship to adapter files
Active rules inai-tools/ai-rules/ are reference material. Canonical enforcement happens through the adapter files listed in the Adapter Inventory section above. The rules lane exists to hold imported or legacy rule surfaces until they are normalised into the adapter model or retired.
Agent packs
Theai-tools/agent-packs/ directory contains generated outputs from the cross-agent packager. These are reference packs and onboarding aids, never canonical sources of repo governance policy.
Generation
Pack structure
The
skills/ subdirectory contains exported copies of all managed skills (currently 53 portable skill exports, live count 2026-05-25; corresponds to 35 local SKILL.md files under ai-tools/ai-skills/<skill>/) with their SKILL.md files and, where applicable, references/ companion bundles.
One-command audit
Agent onboarding path
- Read
docs-guide/policies/agent-governance-framework.mdxanddocs-guide/policies/root-allowlist-governance.mdx. - Use the approved runtime target for the chosen agent family (see Adapter Inventory above).
- Optionally run
node operations/scripts/integrators/ai/agents/cross-agent-packager.js --agent-pack allto generate supplemental reference packs underai-tools/agent-packs/. - Run
node operations/scripts/dispatch/governance/repo/repo-audit-orchestrator.js --mode static --scope fullas the first verification run.
Codex planning entry point
- Skill:
agentic-project-management-orchestrator - Triggers:
skill-plan,plan this repo change,full plan for this - Use this as the first step for ambiguous repo-change planning, especially when the work may touch docs content, navigation, scripts, generated indexes, or governance surfaces.
Fact-check research workflow
The page-content research workflow is the active docs fact-checking skill family. Canonical operator references:- Internal runbook:
docs-guide/frameworks/research-skill-workflow - Public contributor page:
v2/resources/documentation-guide/research-and-fact-checking - Research-to-plan template:
docs-guide/tooling/research-to-implementation-plan-template.md
docs-research-to-implementation-plan follow-on skill.