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Evaluate

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sharpdeveye
evaluate

Use when the user wants a quality review, interaction audit, or to test the workflow against realistic scenarios.

Overview

Publishersharpdeveye
Repositorymaestro
Skill nameevaluate
Stars
591
Forks
73
Bundled files
Instructions only
LicenseMIT
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 sharpdeveye on GitHub. Read the source before you install it.

Installation

Install the Evaluate 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/sharpdeveye/maestro/tree/main/source/skills/evaluate
  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/sharpdeveye/maestro.git /tmp/maestro
mkdir -p .claude/skills
cp -r /tmp/maestro/source/skills/evaluate .claude/skills/evaluate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evaluate 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 Evaluate 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 Evaluate 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.

MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first. Consult the feedback-loops reference in the agent-workflow skill for evaluation patterns, golden test sets, and regression detection.


Evaluate the workflow's actual interaction quality by testing it against scenarios that represent real usage.

Evaluation Dimensions

1. Task Completion

  • Does the workflow actually accomplish what it's supposed to?
  • Does it handle the complete task or only the happy path?
  • Are edge cases addressed or silently dropped?

2. Output Quality

  • Is the output accurate, complete, and well-formatted?
  • Does it match the defined output schema (if any)?
  • Would a domain expert approve the output?

3. Error Behavior

  • What happens when input is malformed?
  • What happens when a tool fails?
  • What happens when the model is uncertain?
  • Is the error message useful or generic?

4. User Experience

  • Is the interaction natural and intuitive?
  • Are confirmations requested for destructive operations?
  • Is the response time acceptable?
  • Does the workflow communicate its limitations?

5. Consistency

  • Does the same input produce consistent output quality?
  • Are there random failures that aren't reproducible?
  • Does quality degrade over long conversations?

Scenario Testing

Create and run test scenarios:

ScenarioInputExpectedActualGrade
Happy pathNormal inputCorrect output?A-F
Edge caseUnusual inputGraceful handling?A-F
Error caseBad inputHelpful error?A-F
Stress caseLarge/complex inputReasonable handling?A-F
AdversarialTricky/malicious inputSafe response?A-F

Evaluation Report

Produce a structured report with:

  1. Overall quality grade (A-F)
  2. Per-dimension scores with evidence
  3. Specific scenario results
  4. Priority improvements with recommended Maestro commands

Evaluation Checklist

  • All 5 dimensions tested with concrete scenarios
  • At least one edge case and one adversarial case tested
  • Results documented in the scenario table
  • Overall grade assigned with justification
  • Improvement actions reference specific Maestro commands

Recommended Next Step

After evaluation, run /fortify to address error behavior gaps, /refine for output quality improvements, or /iterate to set up continuous quality monitoring.

NEVER:

  • Evaluate theoretically — run actual scenarios
  • Give an A grade unless the workflow handles all scenario types well
  • Skip adversarial testing for user-facing workflows
  • Evaluate only the happy path

Frequently asked questions

What does the Evaluate AI skill do?

Use when the user wants a quality review, interaction audit, or to test the workflow against realistic scenarios.

Why use Evaluate on TypingMind?

Because you install it once and use it with any model. Evaluate 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 Evaluate in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sharpdeveye/maestro/tree/main/source/skills/evaluate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Evaluate?

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 Evaluate?

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

Is the Evaluate AI skill free?

Yes. It is published on GitHub by sharpdeveye under the MIT 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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