ai-personnel-assessment

Plugin ai-personnel-assessment from marketplace io-skills in OpenMatter-Network/agent-io-skills. Commands read from the repo's manifest — 1 month ago.

Skills (8)

• ai-audit-meta-components

  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 (conforma

• ai-audit-planning

  Use when scoping or commissioning a psychological audit of an AI/ML personnel assessment — to define which claims the audit will evaluate (validity, utility, lack of bias), establish the auditor's stance and credibility (internal / external / independent), decide formative vs. summative timing and t

• ai-audit-reporting

  Use when writing up and releasing the results of a psychological audit of an AI/ML personnel assessment — producing a precise, comprehensive technical report for testing professionals AND a layperson-friendly summary for those the predictions affect, establishing the auditor's standards and credibil

• 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 ef

• ai-fairness-lenses

  Use FIRST when evaluating, auditing, or debating whether an AI/ML personnel assessment is "fair" or "unbiased" — to define and defend which meaning of fairness/bias applies before drawing conclusions. Covers the three lenses from Landers & Behrend (2023): individual attitudes (distributive/procedura

• 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")

• 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-

• ai-model-outputs-audit

  Use when auditing the scores an AI/ML personnel assessment produces — Component 6 of the Landers & Behrend (2023) framework. Covers evaluating the quality of model predictions: reliability (consistency over time and repeated administrations), validity evidence (do scores reflect the claimed construc