user-invocable: false description: > 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. allowed-tools: Read, Glob, Grep, Edit context: fork agent: copilot-studio-test

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:

   Column	Meaning
   question	The test utterance
   expectedResponse	Expected response (may be empty)
   actualResponse	What the agent responded
   testMethodType_1	Eval method (e.g., GeneralQuality)
   result_1	Pass or Fail
   passingScore_1	Score threshold (may be empty)
   explanation_1	Why 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.