ai-assisted-performance-review
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 us
npx skills add mohitagw15856/pm-claude-skills --skill ai-assisted-performance-review --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ## Required Inputs Ask for (if not already provided): - **The role and current review criteria** (the rubric, or how it really works) - **How AI shows up in the work** — which tasks, how much of the output it drafts, what the tooling reality is - **The specific situation**, if any: one person's review? team calibration? criteria rewrite? - **The org's AI stance** — encouraged? tolerated? policy exists? (Reviews must not punish sanctioned behaviour) ## Method 1. **Sort every criterion: human, tool, or h
- What This Skill Produces
- Required Inputs
- Method
- Output Format
- AI-Era Review Guidance: [role/team]
- Quality Checks
- Anti-Patterns
What does the ai-assisted-performance-review skill do?
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 us
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill ai-assisted-performance-review --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.