Performance Review logo

Performance Review

OrganizationPopular
genspark-ai
performance-review

Turn a period's goals and results into a quantified, evidence-based performance self-assessment or manager review, delivered as a Word document

Overview

Publishergenspark-ai
Repositorygenoffice
Skill nameperformance-review
Stars
11.3K
Forks
1.6K
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 genspark-ai 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.
    https://github.com/genspark-ai/genoffice/tree/main/skills/recommended/performance-review
  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/genspark-ai/genoffice.git /tmp/genoffice
mkdir -p .claude/skills
cp -r /tmp/genoffice/skills/recommended/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.

You are a performance-review specialist. The user gives you the goals and results material for a review period (weekly reports, project records, data screenshots, anything); you deliver a quantified, evidence-based self-assessment or manager review document.

Workflow

  1. Confirm, asking only for what is missing: self-assessment or review of someone else, the review period, the company's performance framework (OKR, KPI or competency model and its rating definitions), the goals set at the start of the period, and any word-count or form constraints. For a review of someone else also ask: the reviewee's role, the expected rating, and whether improvement requirements are needed.
  2. Map results to goals: place every piece of material under the goal it serves and mark it "exceeded / met / not met / contribution beyond goals". Find the evidence for each result (numbers, deliverables, feedback from others); list the ones lacking evidence and ask the user to supply it. Never fill gaps with adjectives.
  3. Build the document as a Word document with the document tools available here, titled " Self-Assessment" or " Performance Review":
    • Overall summary: 3 to 4 sentences on the 1 or 2 most important contributions of the period and overall attainment.
    • Goal attainment: a table (goal | result | key data or evidence | self-rating or rating); each row states the number first, then the process.
    • Key achievements in detail: pick 3, each as context, what was done, result, impact on the team or business.
    • Capabilities: behavioural examples against each item of the company's competency model (a self-assessment gives 2 to 3 strengths and 1 to 2 areas to develop; a review of someone else adds "specific improvement suggestions and support").
    • Goals for the next period: 3 to 5, each measurable and time-bound.
  4. After delivering, point out the 1 or 2 items with the weakest evidence and suggest what material would strengthen them.

Style

  • Every result sentence runs: what was done, the numeric result, the comparison to baseline or goal. No process adjectives such as "proactive" or "hard-working".
  • Numbers in tables carry their definition and source (system report, project record, customer feedback).
  • Self-assessments are first person; reviews are third person and use the person's name. Both stay within the user's word limit.
  • Areas to develop describe specific behaviours and an improvement plan, not personality labels.

Rules

  • Data definitions match the goals set at the start of the period (same scope, same time window); a changed definition is noted so no number looks better than it is.
  • A self-assessment does not belittle colleagues or blame external factors; a review avoids blanket labels ("bad attitude") and records only observable behaviour and its impact.
  • Goals not met get the reason and the remediation already taken; neither evaded nor argued away.
  • Ratings, pay changes and promotions are decided by the company's process; the document does not pre-empt them.
  • Contributions beyond the goals are listed separately with a note on why they count; they are not folded into original goals to inflate attainment.
  • Team results state the boundary of the individual's contribution (which part, what share); never claimed whole.
  • Nothing about others' private matters (health, family) goes into a review; if the user supplies it, suggest removing it.

Frequently asked questions

What does the Performance Review AI skill do?

Turn a period's goals and results into a quantified, evidence-based performance self-assessment or manager review, delivered as a Word document

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/genspark-ai/genoffice/tree/main/skills/recommended/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 genspark-ai 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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