Skill Coverage Gate

Given a SKILL.md and one or more captured trajectories in which it was loaded, translate its instructions into discrete behavior constraints, then report — per constraint — whether the trajectory exercised it (coverage), left it untested, or violated it while in scope (compliance). This is a cheap, single-trajectory adequacy check that slots into the skill-forge ship/review loop before more expensive paired or masked evaluation is worth running.

Why

skill-forge validate + critique check a skill's frontmatter and prose; they never observe whether the skill's rules actually got tested by the run that loaded it. A skill can pass authoring review, ship, and then have half its instructions never fire in any real trajectory — untested behavior masquerading as verified behavior. Worse, an instruction can fire and be disobeyed without anyone noticing, because the task still passed. This gate makes both gaps legible: what was tested, and where the agent already broke a rule it loaded.

Data classes touched

Inputs are project-internal: the skill file plus trajectories captured by session-retro / session-observatory-live under .planning/patterns/. Those traces can quote real work that includes strongly-marked never-surface records — private origins, Fox Companies IP, credentials, Center Camp trust rules. Boundary rule: the coverage report cites constraint evidence by trace location and event index, never by pasting trace content that may carry such a record. The report is project-internal and is written under .planning/; it is never merged into published docs or any shared output.

How

1. Verify the input is valid. Confirm the skill was in the loaded skill set for each supplied trajectory (check the session-retro loaded-skills field). A trajectory where the skill was not loaded gives coverage undefined — discard it, do not count it as zero coverage. Require ≥1 valid trajectory.
2. Extract constraints. Read the skill body and decompose it into atomic behavior constraints, each as {id, precondition, required-behavior, scope-tags}. scope-tags mark whether the constraint governs shared repo state (git worktrees, refinery-merge queue, commits), sensitive memory (Grove / MEMORY.md never-surface records), or self-modification (.claude/skills, .claude/agents, .claude/hooks). This decomposition is an LLM judgment — see the failure model.
3. Label each constraint against the trajectory set.
   • untested — no trajectory ever hit the precondition. An adequacy gap.
   • exercised — precondition fired and the required behavior was observed followed. Counts toward coverage.
   • violated — precondition fired and observed behavior contradicts the required behavior. A compliance failure.
4. Compute two numbers. Coverage = exercised / (exercised + violated + in-scope-but-ambiguous), over constraints whose precondition fired at least once. Compliance = 1 − violated / (exercised + violated).
5. Decide the report verdict.
   • Coverage < 0.60 of extracted constraints → flag under-exercised: recommend more probe trajectories or hand off to skill-counterfactual-audit. Non-blocking — this surfaces the gap.
   • Any violated constraint whose scope-tags include shared repo state, never-surface memory, or self-modification → fail-closed: block the skill-forge ship, do NOT silently proceed, and escalate to the operator via mayor-coordinator.
   • A violated constraint on benign scope → report as a compliance gap, non-blocking, and recommend a skill edit or a narrower trigger.
6. Emit report to .planning/patterns/skill-coverage/<skill>-<timestamp>.md: the constraint table (id, label, evidence-location), the two numbers, and the verdict.

Robustness rule

Judge a constraint exercised or violated by the effect of what the agent did, not by whether the trace text echoes the skill's wording. An agent that paraphrases a rule and follows it is compliant; one that quotes the rule and then acts against it is a violation. Do not mark coverage from surface vocabulary match.

Confidence / failure model

Constraint extraction and the exercised/violated labeling are LLM judgments (semi-decidable, not a deterministic pass/fail check). Coverage from a single trajectory is a lower bound on adequacy, not proof of correctness: high coverage means the tested rules fired, not that the skill is good — that is skill-counterfactual-audit's and skill-causal-curation's question. The gate reduces the chance an untested or already-violated rule ships; it does not eliminate it. On any uncertainty about whether a scope-tagged constraint was violated, default fail-closed (escalate), never silently pass.

When to skip

• No valid trajectory exists where the skill was loaded — capture a run first; there is nothing to measure adequacy against.
• The skill is a pure metadata/frontmatter change — route to skill-frontmatter-doctor, which needs no trajectory.
• A structural gate (skill-forge validate/critique) already failed the skill — fix that first; coverage over a broken skill is noise.

Distinctness

• skill-counterfactual-audit runs a paired with/without probe and measures the skill's behavioural EFFECT (the SIP report). skill-causal-curation uses randomized masking to measure per-task-type CONTRIBUTION for keep/repair/ retire. This skill asks neither — it asks single-trajectory ADEQUACY: were the skill's own rules exercised, and were any broken. It reports gaps; it does not auto-retire or auto-block on effect.

Integration

• skill-forge — this gate runs inside its ship/review loop, after validate + critique, before publish; a fail-closed verdict halts the ship.
• session-retro / session-observatory-live — the trajectory source under .planning/patterns/.
• skill-counterfactual-audit + skill-causal-curation — sibling eval skills answering the EFFECT and CONTRIBUTION questions; escalate an under-exercised skill to them when a single trajectory is not enough evidence.