# AI Claims And Stakeholder Audit

> - Use when auditing how an AI/ML personnel assessment is described and how it affects people — Components 7-9 (information & perceptions) of the Landers & Behrend (2023) framework. Covers first-party developer claims (do they honestly and transparently follow from the audit evidence?), second-party effects on those assessed (candidate reactions, justice, false positives vs. false negatives, what is communicated), and third-party understanding (employment-law experts, regulators, community, public). Triggers: "developer marketing claims vs evidence", "candidate reactions to AI hiring", "applicant fairness perceptions", "false positive vs false negative impact", "what do regulators/public think", "transparency of AI hiring claims".

## Facts
- Page: https://tashan.sh/capability/skill-openmatter-network-ai-claims-and-stakeholder-audit
- tashan id: skill:OpenMatter-Network/ai-claims-and-stakeholder-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-claims-and-stakeholder-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.