eval-dataset

eval-dataset is an LLM evaluation & observability skill for AI agents, published by opendatahub-io in the agent-eval-harness repository on GitHub. Skillspace rates eval-dataset C (77/100) and it passed the safety scan; the rating is based on static analysis only.

Generate evaluation test cases for an eval.yaml. Sources cases per generation.strategy - skill analysis (default), synthetic LLM generation from generation prompts (documentation and agent-capability evals), or MLflow production traces. Bootstraps a starter dataset or augments an existing one to improve coverage. Use when setting up evaluation, when the user needs test cases, when coverage is too thin, or after /eval-analyze when no dataset exists yet. Triggers on "create test cases", "generate test data", "need test inputs", "make a dataset", "add more cases", "improve coverage", "generate documentation eval cases". Also useful when /eval-run reports "no test cases found."

Grade

C

Quality

77 / 100

Safety

Passed

GitHub stars

34

Unproven: rated by static analysis only, not yet compared head-to-head.

Not launch-quality. It is graded C with a clean safety verdict, but our quality filters exclude it: it looks like a template or a stub that was never filled in. So it is not in our launch-quality set, the skills our MCP server searches and recommends. How launch-quality is decided

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.