# Skill Governor > Govern an agent skill portfolio by discovering installed skills, mapping capabilities and boundaries, reviewing overlaps, recording provenance and task evidence, auditing readiness, and preparing reversible upgrades. Use when skills are missing, duplicated, stale, newly installed, being evaluated, or selected for a multi-stage workflow. - Skill: `kazeizumi/skill-governor` (Agent Skill, multi-file: 23 files) - Install (CLI): `npx skillmds@latest add kazeizumi/skill-governor` - Raw SKILL.md: https://api.skillmd.com/api/skills/kazeizumi/skill-governor/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: kazeizumi (https://skillmd.com/u/kazeizumi) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kazeizumi/skill-governor --- # Skill governor Act as the skill manager. Own the installed skill portfolio and decide which skill is fit for a requested capability, stage and boundary. Do not choose a skill because its name happens to contain a prompt keyword. The governor and `ai-creation-workflow` are the two top-level roles in this package. The governor manages skills; the workflow controller manages a project's plan and execution. For a production stage, the workflow controller sends the required output and constraints to the governor, and the governor returns a primary skill, optional specialist, boundary and input requirements. The governor does not take over project scheduling or progress reporting. ## Normal routing 1. Receive a stage request from the workflow controller: required output, inputs, tool, constraints and acceptance evidence. 2. Identify the functional lane and production stage. 3. Run `scripts/skill_registry.py report --lane ""` or inspect the roadmap. 4. Exclude entries whose mapping is stale, colliding or incomplete. 5. Return one primary skill, optional complementary skills, boundaries and missing inputs. The workflow controller writes this assignment into its plan. Reuse an accepted allocation when the lane, requested artifact, input type, tool, constraints and behavior fingerprints are unchanged. Do not rescan the whole portfolio for every project step. Rescan or compare candidates when a new capability appears, a mapping changes, the selected skill fails or the workflow controller reports a real gap. For an auditable full-workflow decision, create a capability request from `assets/templates/capability-request.json`, then use `scripts/capability_gap.py assess`. It produces `fit | partial | gap`, a request fingerprint and at most five local candidates. Read [references/capability-gap.md](references/capability-gap.md) only when the result is not a reusable fit or the user supplied a candidate. ## Capability gaps and candidate adoption Search in this order: unchanged accepted decision, functional roadmap, local installed metadata, then online metadata only for a real unresolved gap. A user-supplied name, path, archive or URL skips discovery search but never skips duplicate, unique-gain, license, safety, dependency, permission or compatibility review. Online discovery never installs or activates its result. Every final third-party candidate receives one recommendation: `full_install`, `reference_strengthen`, or `reject`. Prefer reference strengthening when the candidate substantially overlaps an existing Skill; extract only licensed, decision-changing material and do not create a duplicate directory. Prefer full installation only for a distinct, measurable capability; stage and test it, then start it as `probation`. Reject candidates with no real gain or unresolved safety/license blockers. Honor an explicit user choice without asking again. If the user requested a Skill but did not choose the adoption mode, show the recommendation and ask one compact question: "完整安装，还是参考后补强现有功能？" The choice authorizes the mode, not bypassing a failed safety or license gate. ## Portfolio maintenance ```bash python scripts/skill_registry.py scan --no-plugins python scripts/skill_registry.py report --include-unready python scripts/skill_registry.py duplicates python scripts/skill_registry.py validate python scripts/skill_registry.py package-check python scripts/skill_audit.py audit --output-md skill-audit.md ``` Edit `registry/skill-roadmap.json` after reading the candidate's full `SKILL.md` and any behavior-bearing references. Then rerun `scan` and acknowledge the mapping against its current fingerprint: ```bash python scripts/skill_registry.py ack-map --skill NAME --review-level full-reviewed ``` Record outcomes only after real use. Keep usage frequency separate from quality evidence; neither one substitutes for an artifact review. ## Safe upgrades Never overwrite a locally modified skill with a blind copy. Use `scripts/skill_transaction.py prepare` for a three-way comparison, resolve all conflicts in staging, then create a verified backup before activation. Preserve source commit, license and local modifications in `skill-sources.json`. For retirement, copy `assets/templates/retirement-evidence.json`, complete every replacement, A/B, unique-capability, usage, dependency and license field, then run `prepare-retire --evidence-file ...`. Review its plan, then run `retire --approved retire`. The command verifies the unchanged evidence contract and Skill fingerprint, moves it to a dated archive and records the transaction in `skill-retirements.json`. Use `restore-retired --approved restore` to reactivate that verified archive. System and plugin-cache skills are protected. Read [references/governance.md](references/governance.md) before merging, disabling or replacing a skill, [references/evolution.md](references/evolution.md) when recording learning, scoring or retirement evidence, and [references/registry-schema.md](references/registry-schema.md) when adding routes or evidence. Read [references/role-model.md](references/role-model.md) when deciding whether a change belongs to the skill manager or the workflow controller.