# AI Input Data And Design Audit

> - 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 representative", "ground truth validity", "why are these features predictors", "criterion definition in AI hiring", "range restriction in algorithm training data".

## Facts
- Page: https://tashan.sh/capability/skill-openmatter-network-ai-input-data-and-design-audit
- tashan id: skill:OpenMatter-Network/ai-input-data-and-design-audit
- Source: https://github.com/OpenMatter-Network/agent-io-skills
- Type: skill
- Category: other
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 71.0
- Freshness: 76.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- License: MIT
- Official: no

## Install

```sh
cp -r ai-input-data-and-design-audit ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-08-15 by tashan (https://tashan.sh) from public evidence. Scorer s5.