# AI Model Development Audit

> - Use when auditing how an AI/ML personnel assessment was actually built once its design was set — Components 3-5 of the Landers & Behrend (2023) framework: model development (refinement and cross-validation strategy, documentation), model features (feature engineering — NLP tokens vs. topics, speech-to-text, extracted facial/voice features), and model processes (the estimation algorithm, alternatives explored, and stress tests for bias). Triggers: "audit feature engineering", "k-fold vs holdout vs temporal validation", "NLP bag-of-words bias", "speech-to-text reliability", "how was the model refined", "stress test the algorithm for bias", "model development documentation".

## Facts
- Page: https://tashan.sh/capability/skill-openmatter-network-ai-model-development-audit
- tashan id: skill:OpenMatter-Network/ai-model-development-audit
- Source: https://github.com/OpenMatter-Network/agent-io-skills
- Type: skill
- Category: devtools
- 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-model-development-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.