Create a new EvalView test case YAML file for an agent. Call this when the user asks to add a test, or when you want to capture expected agent behavior. After creating a test, call run_snapshot to establish the baseline. No YAML knowledge required — just describe the test. IMPORTANT: Automatically detect test_path by looking for a 'tests/evalview/' directory in the current project. If found, use it. Otherwise use 'tests'.

Check for regressions against the golden baseline. Returns a diff summary for each test: PASSED, OUTPUT_CHANGED, TOOLS_CHANGED, or REGRESSION. REGRESSION means the score dropped significantly — treat this as a blocking failure. TOOLS_CHANGED / OUTPUT_CHANGED are warnings: the agent's behavior shifted but may be intentional. Also returns observability signals: behavioral anomalies (tool loops, stalls), trust scores (benchmark gaming detection), and coherence issues (multi-turn context loss). Use this after any code change (prompt, model, tools) to confirm nothing broke. If you see a regression, show the diff to the user and offer to fix it before moving on. Use heal=true to auto-retry flaky failures and distinguish non-determinism from real drift. IMPORTANT: Automatically detect test_path by looking for a 'tests/evalview/' directory in the current project. If it exists, pass it as test_path. If the project has a custom test location, use that instead.

Run tests and save passing results as the new golden baseline. Use this to establish or update the expected behavior after an intentional change. Future run_check calls will compare against this snapshot. Call this: (1) after creating a new test with create_test, (2) after confirming a behavioral change is intentional, (3) before making large refactors so you have a clean rollback point. Only passing tests are saved — failing tests are skipped with a warning. IMPORTANT: Automatically detect test_path by looking for a 'tests/evalview/' directory in the current project. If it exists, pass it as test_path.

List all available golden baselines in this EvalView project. Shows test names, variant counts, and when each baseline was last updated.

Validate a SKILL.md file for correct structure, naming conventions, and completeness. Call this after writing or editing a SKILL.md before running tests. Returns a list of issues found and whether the skill is valid.

Auto-generate test cases from a SKILL.md file. Call this when the user asks to create tests for a skill — it reads the skill definition and generates a ready-to-run YAML test suite covering explicit, implicit, contextual, and negative test categories. After generating, call run_skill_test to execute them.

Run a skill test suite against a SKILL.md. Executes two evaluation phases: Phase 1 (deterministic) checks tool calls, file operations, commands run, output content, and token budgets. Phase 2 (rubric) uses LLM-as-judge to score output quality against a defined rubric. Call this after writing skill tests or after any change to the skill or agent. Use --no-rubric for fast Phase 1-only checks with no LLM cost.

Generate a beautiful self-contained HTML visual report from the latest evalview check or run results. Opens automatically in the browser. Call this after run_check or run_snapshot to give the user a visual breakdown of traces, diffs, scores, and timelines. Returns the absolute path to the generated HTML file.

Compare two agent endpoints side-by-side on the same test suite. Useful for A/B testing a new model, prompt change, or architecture swap. Returns per-test score deltas, tool diffs, and an optional HTML report.

Open a trajectory diff viewer for a specific test. Shows a side-by-side HTML comparison of baseline vs current behavior — tool calls, parameters, outputs, and score changes. Opens automatically in the browser.