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Analyze evals

Skill microsoft/skills-for-copilot-studio/skills/analyze-evals

Analyze exported evaluation results from Copilot Studio's Evaluate tab. The user provides a CSV file exported from the Copilot Studio UI; this skill parses it, identifies failures, and proposes YAML fixes. No API access or published agent required — just the exported CSV.From its SKILL.md

Install
npx -y skills add microsoft/skills-for-copilot-studio --skill analyze-evals

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SKILL.md

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Analyze Copilot Studio Evaluation Results

Analyze evaluation results exported from the Copilot Studio UI as CSV.

Phase 1: Get Results

  1. Ask the user for the CSV file path if not already provided. The file is typically exported from Copilot Studio's Evaluate tab and named Evaluate <agent name> <date>.csv in their Downloads folder.

  2. Read the CSV file. The in-product evaluation CSV has these columns:

    ColumnMeaning
    questionThe test utterance
    expectedResponseExpected response (may be empty)
    actualResponseWhat the agent responded
    testMethodType_1Eval method (e.g., GeneralQuality)
    result_1Pass or Fail
    passingScore_1Score threshold (may be empty)
    explanation_1Why it passed/failed (e.g., "Seems relevant; Seems incomplete; Knowledge sources not cited")

    The _1 suffix indicates the first eval method. There may be additional methods (_2, _3, etc.) with the same column pattern.

Phase 2: Analyze Results

  1. Focus on failed evaluations (result_1 = Fail, or any result_N = Fail).

  2. For each failure, use the explanation column to understand the issue:

    • "Question not answered" — The agent couldn't handle the question. Check if there's a matching topic or knowledge source.
    • "Knowledge sources not cited" — The agent responded but didn't cite sources. Check knowledge source configuration and SearchAndSummarizeContent nodes.
    • "Seems incomplete" — The response was partial. Check topic flow for early exits, missing branches, or incomplete SendActivity messages.
    • Error messages in actualResponse (e.g., GenAIToolPlannerRateLimitReached) — These are runtime errors, not authoring issues. Flag them to the user as transient failures to retry.

Phase 3: Propose Fixes

  1. For each failure, identify the relevant YAML file(s):

    • Auto-discover the agent: Glob: **/agent.mcs.yml
    • Find the relevant topic by matching the test utterance against trigger phrases and model descriptions
    • Read the topic file to understand the current flow

  2. Propose specific YAML changes to fix each failure. Present them to the user as a summary:

    • Which test(s) failed and why
    • Which file(s) need changes
    • What the proposed change is (show the diff)

  3. Wait for user decision. The user can:

    • Accept all — apply all proposed changes
    • Accept partially — apply only some changes (ask which ones)
    • Reject — discard proposed changes and discuss alternative approaches

  4. Apply accepted changes using the Edit tool. After applying, remind the user to push and publish again before re-running evaluations.

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