skill-ecosystem-doctor

name	skill-ecosystem-doctor
description	Audit and safely repair cross-runtime Skill ownership, loading, duplication, scopes, budgets, lifecycle, quarantine, and retirement. Usage statistics alone belong to skill-usage-stats. Use when explicitly governing the Skill ecosystem; ignore mentions/traces.

Skill Ecosystem Doctor

Treat the local Skill collection as a governed software supply chain. Audit first, plan repairs from evidence, apply only authorized changes, and finish with fresh cross-runtime verification and a durable handoff.

This workflow is at skill maturity, not unattended automation maturity. Do not schedule or silently apply repairs.

Select the mode

User intent	Mode	Routing
Inspect, review, inventory, or diagnose	audit	execute_direct; read-only
Explain what should change	plan	plan_first; no mutations
Fix, unify, quarantine, or retire	repair	plan_first; explicit scope and rollback
Recheck an existing governance file	verify	execute_direct; read-only
Rotate credentials, rewrite history, push, publish, or change remotes	external action	clarify_first unless the current request grants that exact action

If the request mixes modes, run audit before repair. Do not infer repair authorization from a request to inspect or diagnose.

Operating Contract

• Direct actions: read-only discovery, deterministic audits, report drafts, and local validation.
• Escalate before: destructive changes, credential actions, history rewriting, remote publication, or any mutation not named by the current repair request.
• Evidence-backed pushback: challenge a proposed source, deletion, or completion claim only with paths, state queries, tests, ownership records, or a concrete data-loss or security risk.
• Feedback loop: promote repeated false positives, runtime-layout changes, and manual recovery steps into checks, fixtures, references, or evals.

1. Discover before creating

1. Search active roots and source repositories before creating a Skill, governance file, script, alias, or projection.
2. Locate every applicable AGENTS.md or equivalent before editing a source repository.
3. Read runtime contracts and classify each path as canonical source, managed projection, generated cache, or unknown.
4. Record the task goal, context, constraints, done-when conditions, dirty worktrees, runtime versions, and unavailable external permissions.
5. If work will span many files or sessions, use flowguard and keep the handoff outside parent context.

Common roots are discovery candidates, not declarations. Verify them on the current machine; no data means unknown, not a guessed source relationship.

2. Run the deterministic audit

Use an existing governance file when one exists. Otherwise read the governance schema, adapt the example from discovered facts, and show the proposed configuration before writing it.

The Doctor accepts both its portable schema and the deployed Loom-style SKILL_GOVERNANCE_POLICY.json; do not create a second policy when the latter already exists.

For a large deployed catalog, prefer default_scope: "review" with an explicit global_allowlist. Keep specialist Skills in named profiles, bind profiles to project roots only when needed, and enforce an exposure_budget. A retained profile Skill is still canonical and usable on demand; it is not globally injected until a declared profile scope projects it.

From this Skill directory, run:

python3 scripts/ecosystem_doctor.py --governance ./skill-ecosystem-governance.json
python3 scripts/ecosystem_doctor.py --governance ./skill-ecosystem-governance.json --json

Use --skip-loom only when Loom is intentionally outside scope. A missing Loom binary is an error when Loom validation is requested. Use --fail-on-warn for a strict release gate.

For the deployed policy, run the exposure reconciler without --apply first:

python3 scripts/ecosystem_reconcile.py \
  --registry ~/.loom-registry \
  --policy ~/.loom-registry/SKILL_GOVERNANCE_POLICY.json

The dry-run reports trigger hardening, global/project/profile/review exposure, catalog budgets, plugin-state changes, and stale registry state. Run the same command with --apply only during an explicitly authorized repair run. Plugin configuration receives a timestamped backup before its exact boolean values are changed. Re-run the dry-run afterward and require an empty plan.

If the policy declares exact progressive-disclosure splits, inspect them with:

python3 scripts/ecosystem_split.py \
  --registry ~/.loom-registry \
  --policy ~/.loom-registry/SKILL_GOVERNANCE_POLICY.json

Use --apply only after reviewing the extracted headings and destinations.

The audit checks:

• broken roots, links, and local support-file references
• source directories that look like Skills but have no SKILL.md
• declared-name versus directory-name mismatches
• divergent active projections for the same declared name
• dynamic project/worktree projections and additional declared source roots
• physical runtime copies without an exact source pin
• drift in pinned composite materializations
• active retired, quarantined, or projection-denied Skills
• active references to retired entry points
• high-confidence secret-like literals without printing their values
• missing per-Skill governance decisions when decision coverage is enabled
• review-by-default coverage, named profile bindings, and global catalog budgets
• declared enabled/disabled plugin state without rewriting unrelated TOML
• Loom health, projection drift, and pending remote synchronization

When the request concerns Skills that stopped triggering, aged out, or depend on possibly dead external projects, also read lifecycle drift. Treat missing maintenance metadata as unknown evidence, not proof that a Skill is unhealthy.

Treat test-fixture secret patterns as visible warnings, not silent allowlists.

3. Classify findings

Order repairs by security, logic, data integrity, source lineage, and naming. Separate facts from decisions:

• A digest conflict proves different content; it does not prove which copy is correct.
• A physical copy proves unmanaged materialization; it does not prove deletion is safe.
• A secret pattern proves local exposure risk; it does not prove account-side rotation occurred.
• A healthy projection proves installed consistency; it does not prove the upstream source is committed or remotely backed up.

Read the remediation playbook before planning mutations.

4. Produce a repair plan

For every proposed action, record:

• finding and evidence
• owning source repository or unresolved owner
• exact writable files or paths
• authorization level
• reversible alternative and quarantine path
• repository-specific tests
• cross-runtime verification
• stop condition

Use disjoint file ownership for any parallel work. Do not let two agents edit a shared registry, lockfile, manifest, or high-context file.

5. Apply only approved repairs

Safe direct actions are read-only inspection, report generation, local tests, and drafting a plan. During an authorized repair run:

• prefer an independent clean Git worktree for source edits
• patch the canonical source, then regenerate managed outputs
• quarantine before removal and record original path plus digest
• preserve unrelated dirty worktree changes
• migrate genuinely neutral assets before retiring an entry point
• leave review and unbound profile Skills canonical but unprojected
• remove retired registrations, rules, references, projections, and installer sources without creating compatibility aliases
• update generated registries through their owning generator
• stop if the same hypothesis fails three times

Keep usage evidence read-only. When classification depends on local invocation history, run skill-usage-stats or its governance matrix report, then return here for exposure changes.

Never print secrets, overwrite unknown user content, use force push, rewrite history, or claim external credential rotation without direct evidence.

6. Verify and hand off

Run verification from the current session:

1. Run targeted tests for each changed source repository.
2. Run each repository's build and full test gate when applicable.
3. Re-run ecosystem_doctor.py and require zero errors.
4. Re-run ecosystem_reconcile.py without --apply and require no planned changes.
5. Classify every remaining warning with evidence; do not suppress it merely to reach a clean count.
6. Start a fresh Codex session and confirm the active Skill catalog stays within its declared count/description budget without truncation warnings.
7. Confirm Codex and Claude resolve the intended source or exact pin.
8. For retired Skills, scan all active paths and test the relevant installer so reinstall does not restore them.
9. Run git diff --check in every changed Git worktree.
10. Fill the remediation log template.

Use the eval cases when forward-testing trigger boundaries, read-only behavior, secret redaction, retirement, or dirty-worktree handling.

If commit, push, PR, merge, or landing is requested, prepare a review pack. Use review-gate when installed; otherwise present the same evidence and wait for explicit approval unless the current request grants that exact action.

Done when

• canonical ownership is explicit for every in-scope active Skill
• active projections have no unresolved content conflicts or broken resources
• active global Skills and descriptions fit the declared exposure budget
• retired and denied names have no active path or invocation reference
• high-confidence embedded-secret findings are cleared or explicitly blocked
• lifecycle claims distinguish verified, stale, unknown, and externally blocked evidence
• every mutation has a rollback or quarantine record
• fresh source-specific tests and the ecosystem audit pass
• the final reconcile dry-run is empty
• residual warnings and external actions are listed without overstating closure

Gotchas, negative examples, and drift signals

• Do not choose the newest-looking fork automatically. Compare source history, contracts, tests, and ownership first.
• Do not turn a read-only audit into a bulk cleanup. Produce a repair plan.
• Do not replace quarantine with recursive deletion. Preserve a recoverable copy.
• Do not accept “should work” as verification. Run fresh commands.
• Do not automate this workflow after one successful machine repair. Promote only repeatedly stable, deterministic, read-only checks to scheduling.

Patch this Skill when the validator no longer understands an installed layout, the same false positive recurs, a runtime changes projection semantics, or users repeat the same safety correction.