Skills for Real Engineers. Straight from my .agents directory.
39 skills
ai-assisted-performance-review
Agent skill from
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.
How to install ai-assisted-performance-review
install ai-assisted-performance-review
# install the pm-claude-skills pack with the skills CLI
npx skills add mohitagw15856/pm-claude-skills# or add only this skill to Claude Code: copy the folder
skills/ai-assisted-performance-review/
# into
~/.claude/skills/ai-assisted-performance-review/ (all projects)
.claude/skills/ai-assisted-performance-review/ (one project)
1# AI-Assisted Performance Review Skill23The 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.45## What This Skill Produces67- A **what-measures-whom analysis** of the role's current evaluation criteria8- **Rewritten criteria** that measure the human: judgment, verification, outcomes, leverage9- **Calibration rules** for teams with uneven AI adoption10- **Conversation scripts** for the three hard cases1112## Required Inputs1314Ask for (if not already provided):15- **The role and current review criteria** (the rubric, or how it really works)16- **How AI shows up in the work** — which tasks, how much of the output it drafts, what the tooling reality is17- **The specific situation**, if any: one person's review? team calibration? criteria rewrite?18- **The org's AI stance** — encouraged? tolerated? policy exists? (Reviews must not punish sanctioned behaviour)1920## Method21…
Excerpt shown under the repository's MIT license.
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