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This page is the technical governance index for AI tooling in the Livepeer docs repository. It answers: what is the AI tools governance model, where is the registry, how are tools classified, and how do you validate compliance. For the human-facing guide to AI features on the docs site, see docs-guide/features/ai-features.mdx.

Registry

The AI-tools registry is the canonical source of truth for everything under ai-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
Canonical planning references:
  • workspace/plan/active/AI-TOOLS-GOVERNANCE/structure.md
  • workspace/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 to AGENTS.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 3000 for 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
Adapter responsibilities:
  • 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
Concrete implications:
  • .claude/skills/* may stay for Claude discovery, but should be thin wrappers only
  • .github/AGENTS.md remains 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-packet
  • review-fix
  • handover-readiness
  • page-ship
  • repo-cleanup-handover
These dispatcher families are governed targets even where the repo still exposes the underlying work as adjacent atomic skills or workflow pairs.

Skill lifecycle

  1. Author a template in ai-tools/ai-skills/templates/ following the naming convention NN-skill-name.template.md.
  2. Register the new artifact in ai-tools/registry/ai-tools-registry.json with correct lane (templates), lifecycle state (canonical-template), and metadata.
  3. Sync to local skill roots via the sync mechanism (see below).
  4. Export to agent packs via the cross-agent packager.
  5. Validate with the registry validator to confirm coverage and lane alignment.

Sync mechanism

The sync-codex-skills.js script handles safe upsert of templates into Codex-compatible skill directories. Runbook:
Generated metadata per synced skill:
  • agents/openai.yaml is generated deterministically.
  • Optional companion bundles are sourced from directories beside the template file:
    • <template-stem>.references/ syncs to references/
    • <template-stem>.scripts/ syncs to scripts/
    • <template-stem>.assets/ syncs to assets/
  • 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
Do not use the full local Codex install as the authoritative repo inventory.

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

The ai-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 in ai-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

The ai-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

  1. Read docs-guide/policies/agent-governance-framework.mdx and docs-guide/policies/root-allowlist-governance.mdx.
  2. Use the approved runtime target for the chosen agent family (see Adapter Inventory above).
  3. Optionally run node operations/scripts/integrators/ai/agents/cross-agent-packager.js --agent-pack all to generate supplemental reference packs under ai-tools/agent-packs/.
  4. Run node operations/scripts/dispatch/governance/repo/repo-audit-orchestrator.js --mode static --scope full as 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: Primary entrypoints:
The workflow is experimental and advisory-first. Use it for factual verification, contradiction review, claim-family propagation, and research packets, not generic MDX or link hygiene. When research findings need phased execution planning rather than direct fixes, use the docs-research-to-implementation-plan follow-on skill.

Cross-references

Last modified on May 22, 2026