analyze-evals
Skill486 starsChanged 8 months agoMITView on GitHub
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.
In this repository30 other files it gives its agents.
- add-actionSkill
- add-adaptive-cardSkill
- add-generative-answersSkill
- add-global-variableSkill
- add-knowledgeSkill
- add-nodeSkill
- add-other-agentsSkill
- chat-directlineSkill
- chat-sdkSkill
- chat-with-agentSkill
- clone-agentSkill
- create-eval-setSkill
- create-evalSkill
- detect-modeSkill
- directline-chatSkill
- edit-actionSkill
- edit-agentSkill
- edit-triggersSkill
- int-patternsSkill
- int-project-contextSkill
- int-referenceSkill
- list-kindsSkill
- list-topicsSkill
- lookup-schemaSkill
- manage-agentSkill
- new-topicSkill
- run-evalSkill
- run-tests-kitSkill
- test-authSkill
- validateSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 19
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 …
- Distinctive: line 50
Read the topic file to understand the current flow
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Analyze Copilot Studio Evaluation Results
Analyze evaluation results exported from the Copilot Studio UI as CSV.
Phase 1: Get Results
- 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>.csvin their Downloads folder. Read the CSV file. The in-product evaluation CSV has these columns:
Column Meaning questionThe test utterance expectedResponseExpected response (may be empty) actualResponseWhat the agent responded testMethodType_1Eval method (e.g., GeneralQuality)result_1PassorFailpassingScore_1Score threshold (may be empty) explanation_1Why it passed/failed (e.g., "Seems relevant; Seems incomplete; Knowledge sources not cited") The
_1suffix indicates the first eval method. There may be additional methods (_2,_3, etc.) with the same column pattern.
Phase 2: Analyze Results
- Focus on failed evaluations (
result_1=Fail, or anyresult_N=Fail). - For each failure, use the
explanationcolumn 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
SearchAndSummarizeContentnodes. - "Seems incomplete" — The response was partial. Check topic flow for early exits, missing branches, or incomplete
SendActivitymessages. - 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
- 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
- Auto-discover the agent:
- 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)
- 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
- Apply accepted changes using the Edit tool. After applying, remind the user to push and publish again before re-running evaluations.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

