/skill-eval

Answer one named maintenance decision: retain, revise, remove, or insufficient evidence. Choose the measurement that can answer that decision, use the caller's accepted cases and resource envelope, make one scoped recommendation, and stop. A completed evaluation does not require a positive difference.

This is an optional specialist. The repository's selected runner owns execution and bounds; native results own measurements; BD and Git retain their authority. Do not add a core skill, AO evaluation command, scheduler, dashboard, second tracker, or mandatory review merely to run an experiment.

Choose the question

Caller decision	Measurement	What it can establish
Does loading this skill change a specific observable act?	Behavioral probe with scripts/probe-skill.sh	Behavior change on that scenario; not correct code or productivity
Does this package or version improve engineering outcomes at acceptable cost?	Repository-selected controlled coding comparison, such as evals/skills-rpi	Endpoint outcomes and cost on selected tasks; independent completion only when required exact-subject evidence exists
Does a qualified memory update help later work?	Separate frozen-versus-updated memory transfer test	Narrow later-task reuse evidence with skill and runtime held fixed
What happened in ordinary runs?	Existing native accounting and acceptance evidence	Observational failures, repairs and cost; not causal skill benefit

Start from the caller's intended decision, not a mandatory quiz. Reuse an existing accepted decision and scope. For a behavioral question, name one observable action (a file written, tool used, criterion rejected); a belief such as “understands validation” needs translation into an action. For coding or memory questions, name unchanged task acceptance and the maintenance choice.

Procedure

1. Fix the decision and bounds. Name the subject package/version or qualified memory update, relevant cases, allowed runtime and existing aggregate time, trial and cost limits. Do not infer billing enforcement from token counters. Smoke runs, infrastructure retries, interrupted attempts and inner review consume the same declared envelope. A new configuration or context does not renew it. Do not launch live work without caller authorization and bounds.
2. Choose the smallest relevant measurement. Use behavioral probes for acts, coding tasks for engineering outcomes, and separate later sessions for memory. There is no universal two-effort requirement. Keep the deployed model and effort unless the caller's decision concerns effort. Retain easy regression and cost controls; do not weaken the producer to manufacture separation.
3. Freeze and calibrate. Fix task, acceptance, package, model/runtime, environment and grader identities before trials. Executable oracles must accept the intended solution and reject plausible incorrect/no-op solutions. Include genuinely correct and incomplete cases when evaluating judgment. Exposed incidents are development cases, never unseen holdouts by renaming. Broken or leaked cases invalidate affected comparisons; preserve their historical disposition when versioning a correction.
4. Run within the selected consumer's bounds. Equalize instructions, tools and environment across arms apart from the intended variable. Coding trials expose the actual selected package and required resources. A worktree or a prompt prohibition is not runtime isolation. Exclude operator home, production tracker, session history, sibling output and solutions; capture launched configuration and final artifacts outside the worker. Report an incompatible adapter as such; do not build a replacement platform to rescue a result.
5. Read all attempts. Use native runner results and existing accounting; collection must not require another model call or handwritten evaluation. Keep failed, abandoned, blocked, interrupted and missing attempts visible. Wrong identity, changed acceptance, contamination or ambiguous pairing cannot establish comparison proof even when a deterministic check passed.
6. Compare only supported facts. Pair by task and repetition; preserve repetitions within task clusters. Report case outcomes, denominators, uncertainty and failure disposition. Endpoint reward, worker done claim, in-workflow validator PASS and independent acceptance are different facts. Missing review, usage, billing, phase or feasibility evidence stays unknown. A worker following an instruction establishes adherence, not reduced rework or causal benefit. If its task prompt repeats the skill's direction, attribute the observation to the combined instructions, not the skill alone. A passing case far from a failed boundary does not prove the boundary is repaired.
7. Recommend once and stop. State retain, revise, remove or insufficient evidence, the scope and supporting facts, and what remains unproven. A concrete reproduced defect with clean controls can support a provisional narrow repair; general improvement needs held-out comparison. Do not add trials until green, require a positive result, or automatically publish a lesson. Do not remove losing observations or relax acceptance.

Coding and memory readout

Use the development adapter documented in evals/skills-rpi/readout.md, or the caller's existing equivalent. Its report is a rebuildable view, not work authority. The pilot's default insufficient-evidence recommendation is an honest limit; the specialist may make a narrower supported maintenance recommendation and must state its evidence and provisional scope.

• Report endpoint success against all assigned/observed attempts alongside any feasible-task rate. Retain infrastructure invalidity, infeasibility and unknown coverage separately; do not hide them by dropping the denominator.
• Report false completion, false acceptance and needless blocking separately when independent evidence measures them. Clean cases and abstentions are denominators, not opportunities to reward finding-count spray. Unknown is not zero. Deterministic code truth may settle an experimental criterion, while a required native handoff or exact-subject judgment remains unproven.
• Report raw time/cost distributions and total cost of all attempts per accepted outcome. Zero accepted outcomes makes that ratio undefined. Partial Harbor cost is not total billing. Native input includes cached input; native output includes reasoning. Keep counters distinct and never add native totals to Harbor totals or assume parents exclude children. Split producer, in-workflow validation, orchestration and grading only where native identity supports it. State the measurement window and excluded setup/analysis overhead. Fresh contexts can still carry large startup instructions and tool catalogs; use actual input accounting when available, not freshness as a cost proxy.
• Use evals/_stats for paired task-cluster uncertainty after verifying its dependencies and semantics. A pilot is descriptive unless sample size and decision thresholds were justified and fixed in advance. A zero-crossing interval or no_change is not equivalence; equivalence needs its own margin and test. Same numeric repetitions/seeds do not prove controlled provider randomness. Do not extrapolate local results across libraries or models.
• For memory, hold skill/runtime fixed and compare frozen with independently qualified updated memory in fresh later sessions, using an unseen transfer task and an unrelated or invalidating control. Count acquisition, qualification, retrieval and downstream trial cost separately. Package available, content delivered, relevant action and later outcome are separate facts. Saving a page earns no benefit credit; coding-pilot completion does not establish compounding.

Raw trials and new proof belong in caller-selected protected external non-Git storage. Only public/sanitized fixtures cleared for that destination belong in Git. Preserve legacy .agents/ evidence. Existing independent support and disclosure review precedes memory import; this skill does not auto-publish transcripts or mutate knowledge from aggregate scores (ADR-0016).

Behavioral probes: preserve their existing meaning

scripts/probe-skill.sh remains the runner for small behavioral regression probes and immutable replay. It exposes an empty workspace and one injected SKILL.md, not a complete installed-package coding trial. Its verdict measures behavior change, never quality uplift or productive engineering completion. Existing ledger entries retain that meaning and their recorded limitations.

Probe form	Use when	Discriminator
Tier 1 — quiz	A decision rule is the caller's behavioral question	The answer/action on the scenario
Tier 2 — seeded task	Applying a discipline in work is the question	Whether the agent acted on a realistic planted defect

Either form may be the starting point. Use references/seeding.md for seeded tasks. Grade the act, never vocabulary copied from the treatment. A floor probe detects at least one act; a multi-defect band needs both lower and upper bounds to catch omission and finding spray. Calibrate against a transcript performing the act without the prelude's wording and one repeating the wording without the act.

The declared treatment_source remains the only arm variable: canonical-skill uses exact SKILL.md bytes and is the mode the coverage gate counts; injected-prelude establishes prelude-only evidence. Live runs use the selected authorized native producer with equal scenario and repetitions. Effort levels are a declared experimental choice, not a prerequisite for every question.

bash scripts/probe-skill.sh --probe <id> --replay
# Only within an already authorized live envelope:
bash scripts/probe-skill.sh --probe <id> --live --capture --reps 3 --output out.json
bash scripts/check-skill-probe-headroom.sh

The existing skill.probe-headroom gate in cli/internal/probeheadroom owns classification and thresholds. Its multi-effort saturation rule remains the legacy gate contract; do not fabricate enough runs to satisfy it or rederive the rule in a new report. Read and report the actual answer:

• SATURATED: the probe cannot distinguish the targeted act. Preserve the observation as a scenario limitation in the RUNBOOK; do not append a skill verdict to the legacy ledger. Do not infer skill value or lack of value.
• FLOOR: treatment did not act. Check the discriminator on a known passing transcript. The result alone does not prove the skill cannot help elsewhere.
• UNMEASURED: no usable measurement, not INERT.
• SEPARATED: the gate found usable headroom. This classification itself does not establish positive treatment benefit; retain the actual probe verdict.

Legacy behavioral ledger rows cite the headroom result, model, effort and sample size. Append one row only under that ledger's existing admissibility rules; preserve a valid INERT or losing result. Small samples remain directional. If producer failure or truncation makes a rep infra (discriminator exit 2), exclude it from the legacy usable behavioral rate and report its count in the all-attempt accounting. Zero usable treatment reps is UNMEASURED, never INERT. This rate convention does not authorize dropping infrastructure attempts from coding-cohort accounting.

Output and completion

One scoped recommendation with the decision, cases, all attempts/coverage, paired outcomes when valid, uncertainty, cost/unknowns and failure disposition. For behavioral authoring, also supply the existing probe package (probe.json, question.md, discriminator.sh, fixtures/, and a prelude only in injected-prelude mode) and its replay result. Use the legacy ledger/RUNBOOK only for their existing consumers. No new per-run worksheet is required.

Done when the requested measurement has reached its accepted stop, the relevant replay/oracle checks discriminate, missing coverage is explicit, and one recommendation answers the named maintenance decision. Insufficient evidence, an adverse result or an incompatible runtime can complete this evaluation; none counts as demonstrated skill benefit.

References

• Behavioral runner and conventions: scripts/probe-skill.sh, evals/skill-probes/README.md.
• Behavioral verdicts and non-verdict incidents: LEDGER.md, RUNBOOK.md.
• Existing coverage and headroom gates: check-skill-probe-coverage.sh, check-skill-probe-headroom.sh.
• Evidence and overclaim limits: ADR-0011, ADR-0016 and RPI traversal.