AP
AI-Assisted Performance Review
🔵 Stable 🕐 updated 2026-07-02
🔷 SkillSpec L3
pm-aiwork
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.
What to give it
▸ 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)
✅ The bar it holds itself to
Every skill in this library self-verifies — these are this skill's own quality checks, straight from its definition.
✓ Every current criterion has a human/tool/hybrid verdict — none skipped as "obviously fine"
✓ New criteria are observable behaviours, not virtues ("catches errors before shipping" not "is diligent")
✓ Verification labour is explicitly valued somewhere — the invisible work made visible
✓ Calibration rules prevent both punishing adoption and punishing non-adoption
✓ The launderer case routes to reliability/accountability, not to relitigating the AI policy
⚠️ What it refuses to do
Do not credit or blame the human for what the model did — walk the work backwards to find the human
Do not keep volume metrics "because they're objective" — they're objective measurements of the wrong thing now
Do not run calibration comparing raw output across uneven adopters — that's a tooling lottery, not a review
Do not treat AI scepticism as a performance problem where use is optional — outcomes are the bar, not enthusiasm
Do not have the accountability conversation without the org's policy in hand — improvised rules in a review are how grievances are born
Install
npx pm-claude-skills add --agent claude # or codex · cursor · gemini · hermes
# or one-line MCP (every skill, any client):
claude mcp add pm-skills -- npx -y pm-claude-skills-mcpRelated skills
🔌 Embed this skill
Drop this on your blog, docs, or site — it renders a "Run this skill" card:
<div data-pm-skill="ai-assisted-performance-review"></div>
<script src="https://mohitagw15856.github.io/pm-claude-skills/embed.js" async></script>💬 Discussion