Skill Detail
Regression test LLM apps and agents with metrics, traces, and eval suites using DeepEval
Run repeatable eval suites against prompts, RAG pipelines, and agents so regressions surface before release.
Code Quality & ReviewMulti-Framework
Code Quality & Review
Multi-Framework
Security Reviewed
⭐ 14.8k GitHub stars
⬇ 1.3k/wk npm
COPY SKILL INSTRUCTIONS (OPTIONAL)
npx skills add agentskillexchange/skills --skill regression-test-llm-apps-and-agents-with-metrics-traces-and-eval-suites-using-deepeval
Uses the third-party skills CLI, not an ASE-owned installer. Check your agent’s compatibility. This copies instructions; complete the upstream tool setup below separately.
At a glance
Tools required
Python or Node.js, API access to an LLM judge or compatible local models, CI optional
Install & setup
Install with
pip install -U deepeval for the primary Python workflow, or use the official deepeval npm package when you need the JavaScript path. Then define eval cases and metrics in code and run the suite locally or in CI.Author
Confident AI
Publisher
Organization
Last updated
Apr 16, 2026
Quick brief
Use DeepEval when the job is to define eval cases, score outputs, and catch regressions in LLM apps before shipping changes. It fits teams that want a code-first evaluation workflow around agents, RAG systems, or chatbots, with reusable metrics and CI-friendly test runs. The scope boundary is evaluation and regression testing, not general observability or a hosted AI platform.