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Get Started Free →Systematic quality assessment using BetterBench 46-criterion framework — 5 benchmarks, 30 papers, 40 web searches
.claude/skills/yogsoth-ai-benchmark-audit/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | -13% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -24% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 23% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -4% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 3% | 0% |
Systematic quality assessment of AI/ML benchmarks using the BetterBench 46-criterion framework, Datasheets for Datasets standards, and established psychometric evaluation principles.
Produce a structured quality report for each target benchmark covering: documentation completeness, construct validity indicators, statistical robustness, maintenance status, and known failure modes.
| Resource | Floor | Target | |----------|-------|--------| | Benchmarks audited | 3 | 5 | | Papers read | 20 | 30 | | Web searches | 25 | 40 |
<HARD-GATE>
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Benchmarks audited | 0 | 5 | PENDING |
| Papers fetched | 0 | 30 | PENDING |
| Papers read | 0 | 20 | PENDING |
| Web searches | 0 | 40 | PENDING |
| Documentation audits complete | 0 | 5 | PENDING |
| Metric decompositions complete | 0 | 5 | PENDING |
| Contamination checks complete | 0 | 5 | PENDING |
| Synthesis reports produced | 0 | 5 | PENDING |
</HARD-GATE>Cannot exit until 80% of all targets met.
a. Gather benchmark paper, documentation, leaderboard via web searches b. Run documentation-audit against BetterBench 46 criteria c. Run metric-decomposition on primary metric(s) d. Run contamination-audit checking known training corpora e. Run artifact-detection tactic if annotation-based benchmark f. Collect findings into per-benchmark report
yamlbenchmark_audit: benchmark_name: string version: string betterbench_score: float # 0-1, proportion of 46 criteria met documentation_grade: A|B|C|D|F metric_analysis: primary_metric: string ceiling_effects: boolean polarity_issues: list contamination_risk: low|medium|high|critical artifact_risk: low|medium|high maintenance_status: active|stale|abandoned key_findings: list[string] recommendations: list[string]
<!-- BEGIN available-tables (generated) -->
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use | | --- | --- | | artifact-detection | Detect annotation artifacts and shortcuts in benchmarks |
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | benchmark-synthesis | Produce final structured audit report | | contamination-audit | Detect train-test data leakage and memorization artifacts | | documentation-audit | Assess documentation completeness against BetterBench/Datasheets standards | | knowledge-acquisition-benchmark-inventory | Identify and catalog all relevant benchmarks in target domain | | metric-decomposition | Decompose composite metrics into constituent signals, analyze polarity and ceiling effects |
<!-- END available-tables (generated) -->
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 38,851 | 6,310 | -84% | 1 | 1 | 0% | 6,227 | 1,839 | -70% | 0 | 0 | — |
case-02 | fail→fail | 41,589 | 6,299 | -85% | 1 | 1 | 0% | 6,227 | 1,951 | -69% | 0 | 0 | — |
case-03 | fail→pass | 37,772 | 28,935 | -23% | 1 | 1 | 0% | 6,217 | 5,427 | -13% | 0 | 0 | — |
case-04 | pass→fail | 22,515 | 11,255 | -50% | 1 | 1 | 0% | 3,581 | 1,684 | -53% | 0 | 0 | — |
case-05 | pass→pass | 17,931 | 23,286 | +30% | 1 | 1 | 0% | 3,129 | 5,326 | +70% | 0 | 0 | — |
case-06 | pass→pass | 17,147 | 18,395 | +7% | 1 | 1 | 0% | 3,071 | 4,050 | +32% | 0 | 0 | — |
case-07 | fail→pass | 15,223 | 5,519 | -64% | 1 | 1 | 0% | 2,623 | 1,982 | -24% | 0 | 0 | — |
case-08 | fail→pass | 17,976 | 5,429 | -70% | 1 | 1 | 0% | 1,040 | 1,275 | +23% | 0 | 0 | — |
case-09 | fail→pass | 11,186 | 4,844 | -57% | 1 | 1 | 0% | 1,777 | 1,709 | -4% | 0 | 0 | — |
case-10 | fail→pass | 9,359 | 3,674 | -61% | 1 | 1 | 0% | 1,431 | 1,473 | +3% | 0 | 0 | — |
case-11 | fail→pass | 7,782 | 3,425 | -56% | 1 | 1 | 0% | 1,274 | 1,466 | +15% | 0 | 0 | — |
case-12 | fail→pass | 10,751 | 6,083 | -43% | 1 | 1 | 0% | 1,755 | 1,867 | +6% | 0 | 0 | — |
case-13 | fail→pass | 14,867 | 13,403 | -10% | 1 | 1 | 0% | 2,228 | 2,936 | +32% | 0 | 0 | — |
case-14 | fail→pass | 29,744 | 20,057 | -33% | 1 | 1 | 0% | 1,198 | 4,028 | +236% | 0 | 0 | — |
case-15 | fail→pass | 20,075 | 24,282 | +21% | 1 | 1 | 0% | 3,201 | 4,522 | +41% | 0 | 0 | — |
case-16 | fail→pass | 15,862 | 53,038 | +234% | 1 | 1 | 0% | 2,523 | 6,119 | +143% | 0 | 0 | — |
case-17 | fail→fail | 27,926 | 40,581 | +45% | 1 | 1 | 0% | 4,137 | 1,548 | -63% | 0 | 0 | — |
case-18 | pass→pass | 7,529 | 4,762 | -37% | 1 | 1 | 0% | 1,100 | 1,518 | +38% | 0 | 0 | — |
case-19 | pass→pass | 20,960 | 17,051 | -19% | 1 | 1 | 0% | 3,180 | 3,464 | +9% | 0 | 0 | — |
case-20 | pass→fail | 16,626 | 7,368 | -56% | 1 | 1 | 0% | 2,766 | 1,331 | -52% | 0 | 0 | — |
case-21 | pass→pass | 10,849 | 12,089 | +11% | 1 | 1 | 0% | 1,717 | 2,703 | +57% | 0 | 0 | — |
case-22 | fail→pass | 19,694 | 37,563 | +91% | 1 | 1 | 0% | 3,206 | 6,969 | +117% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 18 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +45 percentage points is the difference between those two pass rates over the 18 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.