Improve Skill Quality

Turn a failing or unconvincing evaluation into a targeted fix. The single most common mistake in this repo is rewriting skill prose in response to a verdict whose real cause was the eval, the fixtures, or the harness. Classify first, then fix.

When to Use

• An evaluation verdict is a regression, underpowered, or "no credible improvement".
• A skill wins in the isolated arm but not in the plugin arm, or is reported "not activated".
• /evaluate reports "Evaluation ran but produced no results".
• A skill scores well but costs too much (tokens, turns, wall time, plugin menu budget).
• Deciding whether to strengthen or retire a persistently weak skill.

When Not to Use

• Creating a new skill from scratch — use create-skill.
• Creating a new eval.yaml from scratch — use create-skill-test.
• Changing the harness itself (eng/skill-validator, eng/vally-adapter, evaluation*.yml).

Inputs

Workflow

Step 1: Get the evidence before forming a hypothesis

Read [InvestigatingResults.md](../../../eng/vally-adapter/InvestigatingResults.md) for how to download artifacts and read results.json. Extract, per failing stimulus:

• authoritative stimulus-vote W/T/L and separate repeated-run W/T/L
• activation status in the isolated and plugin arms, separately
• the judge's verbatim reason on each losing trial
• whether any trial errored, timed out, or produced empty output

Do not change skill content until you can quote a losing trial and the judge's reason for it. For the other cause classes the evidence is different: harness failures are diagnosed from the job log and the spec, and power problems from the trial record — neither has a losing trial to quote, and demanding one is what sends people rewriting prose instead.

Step 2: Classify the failure

Work down this table and stop at the first row that matches. Rows are ordered by how often the symptom has been misdiagnosed as a skill-content problem — the fixture row is first because a fixture failure also presents as a setup or reliability failure and gets misfiled as one.

A verdict is only a measured result when the comparison was conclusive: adapt.mjs requires zero errored trials, zero unmatched trajectories, and an agreeing summary before it will report a pass or a regression. Confirm that before reading a record as a power problem.

Step 3: Rule out harness and reliability causes

See [references/eval-triage.md](references/eval-triage.md) for the full catalogue. The recurring ones:

• A spec declaring both config: and defaults: is rejected by vally, the job still exits 0, and

the PR comment blames "transient infrastructure". Merge them into one defaults: block.

• An errored trial is not automatically a fixture problem — judge-side auth and session.idle

failures look identical from the verdict and need harness fixes, not SDK pins.

• expect_tools: [bash] on an advisory question forces a restore or build and turns an answer into

a timeout with no quality gain.

• Genuine code-generation stimuli need roughly 360s; a timeout yields empty output, which fails

every grader and hides the real quality signal.

• Unmatched trajectories, an errored trial, or a summary that disagrees make the comparison

inconclusive: the remaining matched trials are biased, so the record is not a measured null and must not be read as a power or content problem.

Step 4: Verify the fixtures before touching the skill

Run python eng/eval-quality/checkevalquality.py — it blocks eleven defect classes that can cost a real result here. Then confirm by hand:

• every fixture behaves as its stimulus assumes — a fixture meant to be healthy builds, and one

meant to be broken fails for the exact reason the stimulus is about and no other;

• every referenced fixture is in the git index (git ls-files), not merely on disk — .gitignore

has silently swallowed committed coverage fixtures;

• a fixture never states the same fact in two places that disagree — a Cobertura report whose

declared line-rate, summary totals and <line> elements differ is the canonical case — or the two arms legitimately read different truths.

Step 5: Check whether the eval could ever have passed

The gate has two independent bars, and confusing them is the usual misdiagnosis:

1. Distinct stimuli ≥ 5. Below that the verdict is reported

underpowered — never a pass, never a regression.

1. **The sign test must reach p ≤ 0.05 over the discordant (non-tie) stimulus votes.** Ties are not

discarded silently; they hold the discordant count down.

So at exactly 5 stimuli a single tie is fatal — it leaves 4 discordant. At 6 stimuli one tie is survivable (5W/1T/0L); at 7, up to two are (5W/2T/0L). A loss is not.

So a positive record with a failing verdict is a power problem, not a content problem. Fix it by adding discriminating stimuli. Raising runs measures reliability for the same task and cannot clear the floor.

Step 6: Check whether the two arms differ at all

An eval that compares the skill against itself measures judge noise:

• A dormancy guard (expectactivation: false) must not also set constraints.rejectskills.

That makes the skilled arm skill-free, so the activation contract cannot observe a hijack. Schema version 4 retains the identical-arm comparison for diagnostics but excludes it from preference inference; unexpected isolated activation still blocks a pass.

• A skill with disable-model-invocation: true is absent from the model-facing skilled arm, so its

direct eval compares two identical arms regardless of whether graders inspect activation or answer content. Cover it through consumer outcomes instead; for example, filter-syntax is covered by run-tests and mtp-hot-reload.

• A grader whose config is missing its required key enforces nothing, so the stimulus has one

fewer assertion than it appears to.

Step 7: Fix skill content against the losing trial

Only now change the skill. Apply the patterns in [references/writing-for-baseline-delta.md](references/writing-for-baseline-delta.md); the ones that most often flip a loss:

• Replace reference prose the model already knows with decisions it would otherwise get wrong.
• Add stop-conditions so a strong skill does not over-apply — but do not over-correct into

answering more narrowly than the baseline did.

• Scale output structure to input size; a dashboard for an 8-test suite loses to a direct answer.
• Require truthful validation reporting; claiming "Build succeeded" after a failed restore is an

automatic loss.

• Verify load-bearing API claims by compiling or probing, not by reading source.
• For cost regressions, gate rare or expensive paths behind references/ reads and size any

orchestration to the user's scope.

Step 8: Fix activation

Activation failures are frontmatter and routing failures, not body failures. See [references/eval-triage.md](references/eval-triage.md). Summary:

Step 9: Re-validate

dotnet run --project eng/skill-validator/src/SkillValidator.csproj -- check --plugin ./plugins/<plugin>
python eng/eval-quality/check_eval_quality.py
./eng/run-skill-evals.sh <plugin> <skill>

Then request the official run by submitting a PR review containing /evaluate (Files changed → Review changes), which binds the run to the reviewed commit. Before declaring a regression on the result, confirm the skill payload actually changed — reruns on byte-identical content have shifted 7W/2T/2L to 4W/5T/2L.

Validation

• For a content fix, a losing trial and the judge's stated reason are quoted in the PR description.
• The failure was classified before any content was edited.
• checkevalquality.py and skill-validator check both pass.
• Distinct-stimulus count clears the power bar for the target effect and observed tie rate.
• Isolated and plugin activation are both reported.
• The PR body records root cause, fix, and validation so the lesson is reusable.

Common Pitfalls

References

• [references/writing-for-baseline-delta.md](references/writing-for-baseline-delta.md) — content patterns that beat the unskilled model
• [references/eval-triage.md](references/eval-triage.md) — symptom, cause and fix catalogue with PR citations
• [eng/eval-quality/README.md](../../../eng/eval-quality/README.md) — the eleven structural gate checks and why each exists
• [eng/vally-adapter/InvestigatingResults.md](../../../eng/vally-adapter/InvestigatingResults.md) — downloading artifacts and reading results.json. This is the current guide; the similarly-named eng/skill-validator/src/docs/InvestigatingResults.md documents the retired skill-validator evaluate schema and does not describe today's results.