Benchmark Audit Strategy
SkillDev toolsSystematic quality assessment using BetterBench 46-criterion framework
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Then ask your AI: use the Benchmark Audit Strategy skill
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/benchmark-audit/SKILL.md and read by ahel’s review.
Systematic quality assessment of AI/ML benchmarks using the BetterBench 46-criterion framework, Datasheets for Datasets standards, and established psychometric evaluation principles.
Purpose
Produce a structured quality report for each target benchmark covering: documentation completeness, construct validity indicators, statistical robustness, maintenance status, and known failure modes.
Budget
| Resource | Floor | Target |
|---|---|---|
| Benchmarks audited | 3 | 5 |
| Papers read | 20 | 30 |
| Web searches | 25 | 40 |
State Ledger
<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.
Available Tactics
- artifact-detection — Probe for annotation artifacts and dataset shortcuts
Available SOPs
- benchmark-inventory — Identify target benchmarks in domain
- metric-decomposition — Decompose composite metrics into constituent signals
- contamination-audit — Detect train-test data leakage
- documentation-audit — Assess documentation completeness (BetterBench/Datasheets)
- benchmark-synthesis — Produce final structured audit report
Execution Guidance
- Inventory Phase: Use benchmark-inventory to identify 5 benchmarks in target domain
- Per-Benchmark Loop (repeat for each benchmark): 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
- Synthesis Phase: Run benchmark-synthesis to produce cross-benchmark comparison
Output Format
benchmark_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]
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| artifact-detection | Detect annotation artifacts and shortcuts in benchmarks |
Available SOPs
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 |
Signals
- GitHub stars
- 469
- Forks
- 37
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
benchmark-audit- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine