Assess integrity of an eval, benchmark, metric, A/B test, holdout, LLM-judge, or user-study claim when the question is whether measurement, comparison, or evidence is independent and trustworthy.
86%
Does it follow best practices?
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Adds up to 20 points to the overall score
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The canonical home for this skill is eval-integrity in code-yeongyu/oh-my-openagent
Provide a read-only audit of whether an evaluation can support its stated claim. Do not alter code,
data, prompts, labels, or study operations. Begin with exactly one verdict line in the form
**Verdict: PASS**, **Verdict: FAIL**, or **Verdict: INCONCLUSIVE**. Emit no other verdict
line and do not restate the verdict later in the report. A favorable score is not itself evidence
of integrity.
review-work.data-scientist.Write the claim in testable form: what comparison, metric, threshold, or causal statement is being made; for which population; and under which decision rule. Map every role before judging it:
| Element | Record |
|---|---|
| Claim | The conclusion and decision it is meant to justify. |
| System under test | Exact model, version, configuration, prompt, policy, or treatment. |
| Dataset and split | Sources, collection dates, inclusion rules, train/dev/test or holdout assignment. |
| Designer | Who selected the task, data, prompts, treatments, metric, and stopping rule. |
| Evaluator or judge | Who or what assigns quality judgments, including model and prompt versions. |
| Scorer | The rule or system that turns outputs or judgments into the reported metric. |
| Subjects | Humans, records, tasks, or units exposed to treatment or observation. |
Trace shared ownership, shared data, shared prompts, shared labels, and shared selection decisions across this map. Several models do not establish independence merely because there are several of them; identify distinct training, tuning, prompt design, labels, data, and decision authority.
Identify the independent ground truth that could confirm the claim without reusing the system's own preferences, outputs, labels, or optimization target. Test whether it is:
If no independent ground truth exists, do not substitute agreement among related systems. Mark the claim INCONCLUSIVE unless the stated claim is explicitly limited to that related system's score.
Check only risks relevant to the mapped design:
For each concern, distinguish documented evidence from an assumption. Missing provenance for data, splits, labels, prompts, evaluator selection, or scoring is INCONCLUSIVE, not evidence that the design was independent.
Do not convert uncertainty into PASS because a metric looks plausible, a result is statistically significant, or multiple related evaluators agree.
Report the verdict first. Then report only findings that fired; do not pad the report with risks that were checked and did not fire. For each fired finding include:
Keep conclusions bounded to the evidence. This audit advises; it does not certify a system, recompute results, or approve deployment.
code-yeongyu/lazycodex
e230762
Canonical home
since Nov 4, 2026
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.