Stats
Actions
Tags
Use when auditing the cross-cutting "meta" considerations of an AI/ML personnel assessment that apply across every other component — Components 10-12 of the Landers & Behrend (2023) framework: cultural context (power differentials, cross-cultural transfer, community participation), respect (conformance to accepted ethical standards — the Standards, SIOP Principles, OECD Principles, UGAI), and research designs (whether the studies behind every empirical claim are methodologically defensible). Triggers: "cross-cultural AI hiring", "power differentials in algorithm design", "ethical standards conformance audit", "are the studies behind the claims valid", "research design integrity of an AI audit", "community participation in AI design".
How this skill is triggered — by the user, by Claude, or both
Slash command
/ai-personnel-assessment:ai-audit-meta-componentsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
These three components are **meta** because they must be considered **across all the other components**,
These three components are meta because they must be considered across all the other components, not as a separate stage. They ask: in what cultural and ethical context does this system operate, and is the evidence behind every claim methodologically sound?
What it is: the broader cultural setting in which the algorithm is used, and whether affected communities had a voice in it.
Questions to ask: Has the broader cultural context been considered? Have members of the community participated in the design of systems that will affect them?
Apply it (focal example): Do power differentials exist between designers, employers, and job candidates? Have cultural assumptions been made? Will development decisions made in one culture be applied to another — and if so, how has the development process been adjusted to prevent cross-cultural application challenges?
Audit emphases:
ai-model-development-audit and to linguistic/cultural equivalence in
candidate-accommodations.What it is: whether the algorithm is developed and used in conformance with generally accepted ethical standards.
Questions to ask: Does its use conform to accepted ethical standards — e.g., the Standards, the SIOP Principles, the OECD Principles on AI, and the UGAI?
Apply it (focal example): What ethical standards do the developers claim to have followed? Is there evidence of decisions actually made following that framework? What evidence is there that individual fairness was a priority during development?
Audit emphases:
ai-fairness-lenses, Lens 2.What it is: the methodological quality of the studies offered to support any claim — the integrity check underneath everything.
Questions to ask: How do the research designs (sampling, experimental design, variable choices, analysis, interpretation) of any supporting studies affect the validity of the conclusions?
Apply it (focal example): For every claim that appears to rest on empirical observation, does the study design support the claim? Were all design decisions defensible from the perspective of modern methodological research? What impact might they have had on the validity of the conclusions?
Audit emphases:
ai-model-outputs-audit — Component 12 asks whether
the study that produced the validity/reliability evidence was itself sound.ai-fairness-lenses (legal/ethical/moral lens) · ai-audit-planning · ai-model-outputs-audit
(psychometric counterpart to Component 12) · ai-audit-reporting ·
candidate-accommodations (linguistic/cultural equivalence)
Source: Landers & Behrend (2023), Table 1 (Components 10–12, "Meta-components").
npx claudepluginhub openmatter-network/agent-io-skills --plugin ai-personnel-assessmentCreates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.