DeepEval: Evaluate LLM Outputs with Your AI Coding Agent

If you build AI-powered features, you’ve felt the pain. You ship a prompt change, and suddenly the model starts hallucinating in production. Or you switch from GPT-4 to Claude, and the output format breaks silently. DeepEval solves this — it’s the testing framework for LLM outputs, now available as an AI agent skill.

What It Does

DeepEval treats LLM evaluation like unit testing. Instead of manually eyeballing outputs, you write test cases and metrics — hallucination score, answer relevancy, faithfulness, toxicity, bias, and more. Your agent runs these against your model’s responses and tells you what broke and why.

The skill gives your coding agent the full DeepEval API reference, so it can write and run evaluation tests inside your editor without flipping to docs.

Installation

npx skills add confident-ai/deepeval

Once installed, your agent can scaffold evaluation tests, integrate them into your CI pipeline, and debug failing metrics — all from your terminal.

Key Features

• 20+ built-in metrics — hallucination, GEval, summarization, answer relevancy, faithfulness, bias, toxicity, and more
• Unit-test style syntax — assert llm_output meets hallucination_score < 0.3
• CI integration — run evaluations in GitHub Actions, GitLab CI, or any pipeline
• Real-time dashboard — DeepEval’s platform tracks evaluation history across model versions and prompt changes
• Synthesize test data — generate evaluation datasets from your existing documents automatically

Why Use It as a Skill?

Switching between your editor and DeepEval’s documentation breaks flow. The skill puts the API reference, metric definitions, and example patterns inside your agent’s context. Ask your agent “write me a faithfulness test for this summarization pipeline” and it generates working code that matches your project’s setup.

The docs are bundled as structured references, so answers match your installed version — no outdated Stack Overflow snippets.

Compatibility

Works with Claude Code, Cursor, Codex, GitHub Copilot, and Windsurf. Any agent that supports the skills.sh protocol can load it.

Related Skills

Looking for more tools in the AI evaluation space? Check out our full skill directory. For more on testing AI applications, read our guide to LLM evaluation best practices.