# AI Dynamic Models And Revalidation

> - Use when an AI/ML selection tool uses "dynamic" models or norms that update frequently (sometimes after every administration), or when deciding how often to revalidate and update norms — Concern 7 of Tippins, Oswald & McPhail (2021). Covers the real-change-vs-instability dilemma, technical-report/documentation updates, score adjustments and grandparenting, disparate-treatment risk from candidates evaluated on different variables, applicant-pool shifts affecting validity/range restriction, and revalidation cadence. Triggers: "dynamic models hiring", "algorithm updates after every administration", "how often revalidate AI", "norms updating", "grandparenting test scores", "candidates scored on different variables", "continuous validation".

## Facts
- Page: https://tashan.sh/capability/skill-openmatter-network-ai-dynamic-models-and-revalidation
- tashan id: skill:OpenMatter-Network/ai-dynamic-models-and-revalidation
- 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-dynamic-models-and-revalidation ~/.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.