eval-datasetlisted

# Eval Dataset Use this skill to build durable eval cases from evidence. Its job is dataset quality control, not diagnosis or app fixing. ## Required Reference Use `skills/eval-engineer/references/eval-datasets.md` for the canonical case schema precedence, optional review metadata, promotion rules, bootstrap guidance for different use cases, and Galileo SDK dataset usage. ## Required Inputs Start from at least one evidence source: - `.galileo/current/debug-packet.json` - `.galileo/current/diagnosis.md` - Galileo trace, session, experiment, or log-stream IDs - production symptom with enough context to define expected behavior If there is no concrete failure, regression, policy requirement, or metric gap, ask for evidence before writing a case. ## Do - Write new unreviewed cases to `.galileo/eval-dataset/candidates.jsonl` unless the user asks to accept or reject a case. - When accepting or rejecting cases, update `.galileo/eval-dataset/changelog.md`. - Bootstrap datasets by use case: RAG, tool-calling agent, multi-turn, workflow, safety/compliance, and tokenomics. - Choose failure triggers that should be caught by named Galileo metrics or explicit local gates. - Follow the user-provided schema when the user gives one. - Follow the existing Galileo dataset schema when appending to a fixed dataset. - Do not force Eval Engineer fields into an upload schema. Put optional review metadata in `.galileo/eval-dataset/candidates.jsonl`, `metadata`, or a sidecar file wh