loading
loading
Build repeatable LLM evals with golden datasets and scoring rubrics.
Most teams ship prompt changes on vibes. This makes model quality a CI gate with golden sets and rubrics, so a regression fails the build like any bug.
Listed for review
No verified public repo for this skill yet, so this page does not give you an install command. Skills with a verified source install in one command — or fully manual: copy the skill folder into .claude/skills/ and your agent picks it up.
Boostor Quality Score
84/100 · B
Eval Harness lets you define test cases in YAML, run them against any OpenAI-compatible endpoint, and track regression across model versions. It integrates with Hugging Face datasets for automatic golden-set management and outputs structured JSON reports. Use it to gate CI on model quality, not just code coverage.
Generate a behavior eval suite for a skill — trigger, anti-trigger, output-contract, and failure cases.
Complements the eval-harness runner: this one writes the cases, including the anti-triggers and failure paths most authors never test.
Transparent + deterministic: every point above is computed from this skill's real fields plus a prompt-injection safety scan. No black box, no pay-to-rank.