{"slug":"openmatter-network-ai-input-data-and-design-audit","source_name":"openmatter-network/ai-input-data-and-design-audit","name":"Openmatter Network/AI Input Data And Design Audit","description":"Use when auditing the data and foundational design of an AI/ML personnel assessment — Components 1-2 of the Landers & Behrend (2023) framework. Covers input-data population, sampling, range restriction, and incumbent-vs-applicant generalizability; and model design: how the criterion (\"ground truth\") is defined and its construct validity, why each predictor/feature was included, and whether choices were theory-driven or empirically derived. Triggers: \"audit training data\", \"is the training sample","version":1,"lift":{"pass_rate_delta_pts":36.36,"pass_rate_pct":100,"total_cases":22,"passed_cases":22,"tokens_delta_pct":46.1,"turns_delta_pct":0,"verdict":"pass","benchmark_model":"gemini-3.6-flash","grading_method":"judged","completed_at":"2026-08-03T14:13:40.017177+00:00"},"skill_score":1,"benchmark_models":[{"model":"gemini-3.6-flash","headline":true,"delta_pts":36.36,"with_pass_pct":100,"without_pass_pct":63.6,"tokens_delta_pct":46.1,"turns_delta_pct":0,"total_cases":22,"cases_aggregated":22,"verdict":"pass","never_hurt":true,"completed_at":"2026-08-03T14:13:40.017177+00:00","run_id":"eda738e5-dee2-4eb8-be8a-6c4af2d74f4b","version_number":1,"is_latest_version":true,"gate":null}],"trust":{"skill_safety":"passed","safety_status":"clean","intent_verdict":"safe","content_status":"clean","indexable":true},"license":"MIT","install_count":0,"manifest_hash":"c856877fa807aa49e35890c1198fa7d8880c90043e9e12bb7b12dde5e08ce75b","raw_url":"https://app.decimal.ai/s/openmatter-network-ai-input-data-and-design-audit/SKILL.md","scorecard_url":"https://app.decimal.ai/skills/openmatter-network-ai-input-data-and-design-audit"}