Create Eval Set logo

Create Eval Set

Organization
microsoft
create-eval-set

Create a test set CSV file for import into Copilot Studio's in-product Evaluate tab. Reads the agent's topics, instructions, and knowledge sources to generate meaningful test cases with appropriate graders (General quality, Compare meaning, Exact match, etc.). Use when the user asks to create, prepare, or generate evaluation test cases for their agent.

Overview

Publishermicrosoft
Repositoryskills-for-copilot-studio
Skill namecreate-eval-set
Stars
478
Forks
101
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 microsoft on GitHub. Read the source before you install it.

Installation

Install the Create Eval Set 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/microsoft/skills-for-copilot-studio/tree/main/skills/create-eval-set
  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/microsoft/skills-for-copilot-studio.git /tmp/skills-for-copilot-studio
mkdir -p .claude/skills
cp -r /tmp/skills-for-copilot-studio/skills/create-eval-set .claude/skills/create-eval-set
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create Eval Set 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 Create Eval Set 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 Create Eval Set 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.

Create Evaluation Test Set

Create a test set CSV file that can be imported into Copilot Studio's Evaluate tab for in-product agent evaluation.

Phase 1: Understand the Agent

Read the agent's YAML files to understand what it does:

  1. Glob: **/agent.mcs.yml — find the agent
  2. Read agent.mcs.yml — get the agent's instructions, description, and capabilities
  3. Read settings.mcs.yml — check orchestration mode (generative vs classic)
  4. Glob: **/topics/*.mcs.yml — list all topics
  5. Read key topics (especially non-system ones) — understand trigger phrases, conversation flows, expected behaviors
  6. Check for knowledge sources, actions, and connected tools

Phase 2: Design Test Cases

Create test cases that cover:

CategoryWhat to testExample
Core functionalityMain topics and capabilitiesQuestions matching trigger phrases
Knowledge/generativeKnowledge source responsesQuestions the agent should answer from its knowledge
System topicsGreeting, Escalation, Goodbye, Thank You, Fallback"Hi", "I want to speak to a person", "Goodbye"
Edge casesOut-of-scope, ambiguous, off-topic"Tell me a joke", "Book a flight for me"
Boundary testingThings the agent should NOT doActions beyond its capabilities

Aim for 10–25 test cases with good coverage across categories.

Phase 3: Write the Expected Responses

The CSV import only supports two columns: question and expectedResponse. Test methods cannot be set via CSV import — they are configured in the UI after import. The default test method (General quality) is applied to all imported test cases.

Write expected responses with this in mind:

  • For questions where you want General quality grading: write behavioral descriptions ("The response should recommend hotels in Paris with relevant details")
  • For questions where you'll later switch to Compare meaning or Exact match in the UI: write realistic agent replies that the grader can compare against
  • Leave expectedResponse empty for questions that only need General quality (it works without expected responses)

Available test methods (configured in UI after import)

Test methodWhat it measuresRequires expected response?
General quality (default)AI-graded quality: relevance, completeness, groundedness, abstentionNo (but recommended as a rubric)
Compare meaningSemantic similarity — compares meaning/intentYes
Text similarityCosine similarity of textYes, configurable pass threshold
Exact matchCharacter-for-character matchYes
Keyword matchResponse contains expected keywords/phrasesYes (keywords added in UI)
Capability useAgent called expected tools/topicsConfigured in UI
CustomCustom grader with your own instructions and labelsConfigured in UI

Phase 4: Write the CSV

Write the CSV file using the Write tool. The format must be:

csv
"question","expectedResponse"
"User question here","Expected agent response or behavioral rubric"
"Question without expected response",

Column specification

ColumnRequiredDescription
questionYesThe user message to send to the agent. Max 1,000 characters.
expectedResponseNoThe expected response or behavioral rubric. Leave empty if not needed.

Important: The Testing method column is not supported on import — it is ignored. All imported test cases get the default test method (General quality). Configure other test methods in the UI after import.

Rules

  • Max 100 questions per test set
  • Max 1,000 characters per question (including spaces)
  • File must be .csv format
  • Use double quotes around all values
  • For questions that will use General quality: write expected responses as behavioral descriptions
  • For questions that will use Compare meaning or Exact match: write expected responses as realistic agent replies

Expected response examples

Behavioral rubric (for General quality):

csv
"Find me a hotel in Paris","The response should include hotel recommendations in Paris with relevant details like names, locations, or prices."

Realistic reply (for Compare meaning — set method in UI after import):

csv
"Hi there","Hello! How can I help you today?"

Exact expected text (for Exact match — set method in UI after import):

csv
"What is 2+2?","4"

Phase 5: Instruct the User

After writing the CSV, tell the user:

To import into Copilot Studio:

  1. Open your agent in Copilot Studio
  2. Go to the Evaluate tab
  3. Click New evaluation > Single response
  4. Drag or browse for the CSV file
  5. Review the imported test cases and adjust if needed
  6. Optionally add more test methods (Capability use, Custom) in the UI
  7. Click Evaluate to run, or Save to run later

After import, some things can only be configured in the UI:

  • Pass thresholds for Compare meaning and Text similarity
  • Keywords for Keyword match test cases
  • Expected capabilities for Capability use test cases
  • Custom grader instructions and labels

Frequently asked questions

What does the Create Eval Set AI skill do?

Create a test set CSV file for import into Copilot Studio's in-product Evaluate tab. Reads the agent's topics, instructions, and knowledge sources to generate meaningful test cases with appropriate graders (General quality, Compare meaning, Exact match, etc.). Use when the user asks to create, prepare, or generate evaluation test cases for their agent.

Why use Create Eval Set on TypingMind?

Because you install it once and use it with any model. Create Eval Set 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 Create Eval Set in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/skills-for-copilot-studio/tree/main/skills/create-eval-set. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Create Eval Set?

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 Create Eval Set?

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

Is the Create Eval Set AI skill free?

Yes. It is published on GitHub by microsoft 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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