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THE AGENSI STORE
4 skills found
An adversarial gate that audits an AI eval or test suite — LLM-judge rubrics, datasets, regression tests, metrics — for gameable criteria, data leakage, missing edge cases, and non-determinism, then returns one PASS/REVISE/FAIL verdict.
Builds a complete LLM evaluation framework — quality dimensions, a golden dataset, code-based and model-graded rubric graders, judge calibration, and CI regression rules. Use when the user says build LLM evals, create a golden dataset, or set up LLM-as-judge. Do not use when they want to debug one bad model output, not build a repeatable measurement system.
Design and write the eval suite for your LLM-powered feature — the metrics that match your failure modes, a golden dataset plan with starter cases, anchored rubrics, LLM-as-judge prompts with the known bias mitigations, and pass/fail gates wired for CI.