ai-assisted-performance-review
帮助管理者公平评估频繁使用AI的员工产出
在绩效评审中区分人类贡献与AI辅助带来的影响
在绩效评估里把人做了什么和 AI 帮了什么区分开，给出能衡量“人”的指标、改写考核标准和面谈话术。当某人大量用 AI、产出量不再能代表工作量或团队 AI 使用不均时就该用。特点是既不会把模型的工作原封不动算到人头上，也不会一味惩罚 AI 使用，给出可执行的校准规则和步骤。
▸ 展开 SKILL.md 英文原文
Evaluate performance fairly when output is AI-assisted — what still measures the human, what now measures the tooling, and how to run the review conversation. Use when reviewing someone whose work is heavily AI-assisted, when output volume stopped meaning anything, when calibrating a team with uneven AI adoption, or when writing review criteria for the AI era. Produces review guidance: a what-measures-whom analysis, rewritten criteria, calibration rules for mixed-adoption teams, and conversation scripts. For the general review document use performance-review; for redesigning the role itself use role-redesign-for-ai.
帮我安装这个 skill：https://raw.githubusercontent.com/mohitagw15856/pm-claude-skills/main/skills/ai-assisted-performance-review/SKILL.mdcurl -fsSL "https://raw.githubusercontent.com/mohitagw15856/pm-claude-skills/main/skills/ai-assisted-performance-review/SKILL.md"# AI-Assisted Performance Review Skill The uncomfortable review question of the decade: when a report ships twice the output with AI, what did *they* do? Volume stopped measuring effort; polish stopped measuring skill. Punishing AI use is as wrong as crediting the model's work to the human. This skill separates the signals — and gives managers the conversation, not just the theory. ## What This Skill Produces - A **what-measures-whom analysis** of the role's current evaluation criteria - **Rewritten criteria** that measure the human: judgment, verification, outcomes, leverage - **Calibration rules** for teams with uneven AI adoption - **Conversation scripts** for the three hard cases ##