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/run-eval

Run EvalView regression checks against golden baselines to detect regressions in AI agent behavior after code, prompt, or model changes.

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evalview
1403 skills1 MCP
Install
$ npx -y skills add hidai25/eval-view --skill run-eval --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/run-eval

Context preview

The summary Claude sees to decide when to auto-load this skill.

Run EvalView regression checks against golden baselines to detect regressions in AI agent behavior after code, prompt, or model changes.

SKILL.md

run-eval.SKILL.md
name: run-eval
description: Run EvalView regression checks against golden baselines to detect regressions in AI agent behavior after code, prompt, or model changes.

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.
Read more
Ships withevalview

Regression testing for AI agents. Snapshot behavior,diff tool calls,catch regressions in CI. Works with LangGraph, CrewAI, OpenAI, Anthropic.

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Python
Language
Apache-2.0
License
8d ago
Last commit
11mo ago
Created
10h ago
Added

Repo: hidai25/eval-view

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generate-tests

Generate EvalView test cases — either from a SKILL.md file using LLM-powered generation, or by capturing real agent interactions…