Skill System Governance

Overview

Treat the skill system as a maintained product: measure discoverability, routing, context cost, ownership, generated bundles, documentation, and validation before changing it. This skill is the standard workflow for the existing 技能维护 role; it does not create another role.

When invoked, start with the baseline audit. Do not ask the user to choose a checklist and do not assume that every review must produce a change.

Default Workflow

1. Establish The Baseline

• Read repository instructions, git status, registry/package ownership, and the relevant role/skill contracts.
• Keep this phase read-only.
• Run the deterministic quick audit from the repository root:

python skills/skill-system-governance/scripts/audit_skill_system.py --repo . --mode quick

• Read references/audit-dimensions.md only for the dimensions relevant to the task.
• If the request depends on current OpenAI model, prompt, plugin, or Codex behavior, verify it against current official documentation. Do not browse by default for repository-local drift.

2. Add Runtime Evidence When Available

Static checks prove structure, not runtime selection quality. When the user supplies observed routing decisions or callback artifacts, run:

python skills/skill-system-governance/scripts/audit_skill_system.py \
  --repo . --mode quick \
  --observed path/to/observed-routing.jsonl \
  --callbacks path/to/callback-a.md \
  --callbacks path/to/callback-b.md

Without those artifacts, report routing accuracy and skill-hit rate as not_evaluable. Never convert self-reported callbacks into observed router accuracy or claim a percentage from chat memory.

For explicitly authorized local lifecycle observation, read references/local-observation.md and use the opt-in installer. It preserves existing hooks and requires native hook trust; metadata snapshots do not prove skill selection, task quality, or membership savings.

3. Produce A Verdict Before Editing

Choose one primary verdict:

• no-change: evidence does not justify a change.
• docs-only: implementation is sound but discoverability or guidance drifted.
• routing: trigger descriptions, role ownership, eval cases, or package selection need correction.
• contract-script: repeated ambiguity needs a deterministic field, enum, guard, or validator.
• package: Core/domain boundaries or context budgets need adjustment.
• consolidate-deprecate: duplicate or obsolete workflows should merge, downgrade, or retire.

Order findings by severity and leverage. For each finding, include evidence, causal mechanism, smallest useful change, and a validation path. Separate measured facts from inference.

4. Apply Only Authorized Minimal Changes

If the user asked only for an audit, stop after the verdict and recommendations. If the user authorized optimization:

1. Edit canonical files under skills/, registry/, docs/, evals/, or scripts/.
2. Do not edit generated plugins/*/skills/ copies directly.
3. Prefer one causal fix over adding repeated prose to several prompts.
4. Add or update deterministic tests before broad documentation changes.
5. Run python scripts/sync_plugin_bundles.py --write after canonical changes.
6. Run the full audit:

python skills/skill-system-governance/scripts/audit_skill_system.py --repo . --mode full

7. Follow repository commit and PR rules. Keep generated bundles in the same PR as their canonical source.

Governance Boundaries

• Do not create a new skill from one anecdote. First decide whether the issue belongs in an existing skill, a role contract, a script, project-local instructions, or nowhere.
• Do not move domain capability into Core merely to make it easier to discover. Core contains cross-domain routing and governance only.
• Do not load the full skill catalog when registry metadata and a scoped reference are enough.
• Do not duplicate a workflow in role prompts. Role cards should route to the owning skill.
• Do not place project-local state such as .codex/role-windows.md into the shared repository.
• Do not treat longer prompts, more roles, or more checks as automatic improvements. Every added instruction needs a failure mode it prevents.
• Do not mutate user installations, plugin caches, or external upstream sources during a repository audit unless separately requested.
• Preserve provenance and public-safety rules. Never add machine paths, secrets, private logs, cookies, or project data.

Default Output

**Verdict**
no-change / docs-only / routing / contract-script / package / consolidate-deprecate

**Evidence**
- Deterministic checks:
- Runtime evidence:
- Not evaluable:

**Findings**
| Priority | Finding | Evidence | Smallest Change | Validation |
|---|---|---|---|---|

**Decision**
- Apply now / defer / no change
- Scope and non-goals

**Verification**
- Commands and results

**Residual Risk**
- Remaining uncertainty

Keep a clean audit concise. Expand only findings that change a decision.

Resources

• scripts/audit_skill_system.py: deterministic read-only quick/full audit and optional runtime-evidence aggregation.
• references/audit-dimensions.md: scoped architecture, routing, Token, packaging, lifecycle, and documentation review criteria.