Performance Review logo

Performance Review

OrganizationPopular
anthropics
performance-review

Structure a performance review with self-assessment, manager template, and calibration prep. Use when review season kicks off and you need a self-assessment template, writing a manager review for a direct report, prepping rating distributions and promotion cases for calibration, or turning vague feedback into specific behavioral examples.

Overview

Publisheranthropics
Repositoryknowledge-work-plugins
Skill nameperformance-review
Stars
24.9K
Forks
3K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by anthropics on GitHub. Read the source before you install it.

Installation

Install the Performance Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/anthropics/knowledge-work-plugins.git /tmp/knowledge-work-plugins
mkdir -p .claude/skills
cp -r /tmp/knowledge-work-plugins/human-resources/skills/performance-review .claude/skills/performance-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Performance Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Performance Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Performance Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/performance-review

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Generate performance review templates and help structure feedback.

Usage

/performance-review $ARGUMENTS

Modes

/performance-review self-assessment       # Generate self-assessment template
/performance-review manager [employee]    # Manager review template for a specific person
/performance-review calibration           # Calibration prep document

If no mode is specified, ask what type of review they need.

Output — Self-Assessment Template

markdown
## Self-Assessment: [Review Period]

### Key Accomplishments
[List your top 3-5 accomplishments this period. For each, describe the situation, your contribution, and the impact.]

1. **[Accomplishment]**
   - Situation: [Context]
   - Contribution: [What you did]
   - Impact: [Measurable result]

### Goals Review
| Goal | Status | Evidence |
|------|--------|----------|
| [Goal from last period] | Met / Exceeded / Missed | [How you know] |

### Growth Areas
[Where did you grow? New skills, expanded scope, leadership moments.]

### Challenges
[What was hard? What would you do differently?]

### Goals for Next Period
1. [Goal — specific and measurable]
2. [Goal]
3. [Goal]

### Feedback for Manager
[How can your manager better support you?]

Output — Manager Review

markdown
## Performance Review: [Employee Name]
**Period:** [Date range] | **Manager:** [Your name]

### Overall Rating: [Exceeds / Meets / Below Expectations]

### Performance Summary
[2-3 sentence overall assessment]

### Key Strengths
- [Strength with specific example]
- [Strength with specific example]

### Areas for Development
- [Area with specific, actionable guidance]
- [Area with specific, actionable guidance]

### Goal Achievement
| Goal | Rating | Comments |
|------|--------|----------|
| [Goal] | [Rating] | [Specific observations] |

### Impact and Contributions
[Describe their biggest contributions and impact on the team/org]

### Development Plan
| Skill | Current | Target | Actions |
|-------|---------|--------|---------|
| [Skill] | [Level] | [Level] | [How to get there] |

### Compensation Recommendation
[Promotion / Equity refresh / Adjustment / No change — with justification]

Output — Calibration

markdown
## Calibration Prep: [Review Cycle]
**Manager:** [Your name] | **Team:** [Team] | **Period:** [Date range]

### Team Overview
| Employee | Role | Level | Tenure | Proposed Rating | Notes |
|----------|------|-------|--------|-----------------|-------|
| [Name] | [Role] | [Level] | [X years] | [Rating] | [Key context] |

### Rating Distribution
| Rating | Count | % of Team | Company Target |
|--------|-------|-----------|----------------|
| Exceeds Expectations | [X] | [X]% | ~15-20% |
| Meets Expectations | [X] | [X]% | ~60-70% |
| Below Expectations | [X] | [X]% | ~10-15% |

### Calibration Discussion Points
1. **[Employee]** — [Why this rating may need discussion, e.g., borderline, first review at level, recent role change]
2. **[Employee]** — [Discussion point]

### Promotion Candidates
| Employee | Current Level | Proposed Level | Justification |
|----------|-------------|----------------|---------------|
| [Name] | [Current] | [Proposed] | [Evidence of next-level performance] |

### Compensation Actions
| Employee | Action | Justification |
|----------|--------|---------------|
| [Name] | [Promotion / Equity refresh / Market adjustment / Retention] | [Why] |

### Manager Notes
[Context the calibration group should know — team changes, org shifts, project impacts]

If Connectors Available

If ~~HRIS is connected:

  • Pull prior review history and goal tracking data
  • Pre-populate employee details and current role information

If ~~project tracker is connected:

  • Pull completed work and contributions for the review period
  • Reference specific tickets and project milestones as evidence

Tips

  1. Be specific — "Great job" isn't feedback. "You reduced deploy time 40% by implementing the new CI pipeline" is.
  2. Balance positive and constructive — Both are essential. Neither should be a surprise.
  3. Focus on behaviors, not personality — "Your documentation has been incomplete" vs. "You're careless."
  4. Make development actionable — "Improve communication" is vague. "Present at the next team all-hands" is actionable.

Frequently asked questions

What does the Performance Review AI skill do?

Structure a performance review with self-assessment, manager template, and calibration prep. Use when review season kicks off and you need a self-assessment template, writing a manager review for a direct report, prepping rating distributions and promotion cases for calibration, or turning vague feedback into specific behavioral examples.

Why use Performance Review on TypingMind?

Because you install it once and use it with any model. Performance Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Performance Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/knowledge-work-plugins/tree/main/human-resources/skills/performance-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Performance Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Performance Review?

As many as you like. As long as a model supports skills, you can use Performance Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Performance Review AI skill free?

Yes. It is published on GitHub by anthropics under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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