run-eval

Run Eval

Use this skill after making changes to an AI agent (prompt edits, model swaps, tool changes, code refactors) to verify nothing broke.

What this does

EvalView compares current agent behavior against saved golden baselines. It runs your test cases, evaluates the outputs, and reports a diff status for each test:

• PASSED — behavior matches the baseline
• OUTPUT_CHANGED — output shifted but may be intentional
• TOOLS_CHANGED — different tools were called
• REGRESSION — score dropped significantly (blocking failure)

Steps

1. Locate the test directory. Look for tests/evalview/ in the project. If it exists, use that. Otherwise check for a tests/ directory with .yaml test files.

2. Run a regression check using the run_check MCP tool:

   • If checking all tests: call run_check with the detected test_path
   • If checking a specific test: also pass the test parameter with the test name

3. Interpret results:

   • If all tests pass, confirm to the user that no regressions were found
   • If REGRESSION is reported, show the diff (score delta, tool changes, output similarity) and offer to help fix it
   • If OUTPUT_CHANGED or TOOLS_CHANGED, flag it as a warning — the user should decide if the change is intentional

4. If changes are intentional, offer to update the baseline by calling run_snapshot with an explanatory notes parameter.

5. Generate a visual report (optional) by calling generate_visual_report for a detailed HTML breakdown of traces, diffs, scores, and timelines.

CLI equivalent

evalview check tests/evalview/
evalview check tests/evalview/ --test "my-test"
evalview snapshot tests/evalview/ --notes "updated after prompt refactor"

Tips

• Use run_check frequently — it calls the Python API directly with no subprocess overhead.
• A score delta near zero with TOOLS_CHANGED often means the agent found an equivalent path.
• Always snapshot after confirming intentional changes so future checks compare against the new baseline.