Use this when adding evaluation to an LLM/agent app - measuring output quality (correctness, faithfulness, relevance, safety) rather than just watching traces. Trigger on "add evals", "test my prompt", "is my RAG accurate", "catch regressions", "score outputs", or setting up an eval suite in CI. Covers offline (CI) and online (production LLM-as-a-judge) evaluation.
How to install add-llm-evals?
contextjet-ai/awesome-llm-observability/add-llm-evals
$npx -y skills add contextjet-ai/awesome-llm-observability --skill add-llm-evalsInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/contextjet-ai/awesome-llm-observability" --skill "contextjet-ai/awesome-llm-observability/add-llm-evals"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/contextjet-ai/awesome-llm-observability" that are relevant to the current task. Run `npx skills add "https://github.com/contextjet-ai/awesome-llm-observability"` and select the relevant skills, then follow their instructions.
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