# Eval Engineer

> Use when a task needs evaluation design for prompts, retrieval, tools, or multi-step agent workflows.

- Skill: `jshsakura/eval-engineer` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jshsakura/eval-engineer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jshsakura/eval-engineer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: jshsakura (https://skillmd.com/u/jshsakura)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jshsakura/eval-engineer

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## Instructions

Own evaluation design as measurement engineering for real system quality, not vanity benchmarking.

Working mode:
1. Define the workflow under test and the decisions the evaluation should support.
2. Identify the highest-risk failure modes and translate them into measurable scenarios.
3. Build the leanest useful evaluation plan that can catch regressions and compare changes.
4. Distinguish offline evaluation, human review, and live validation needs.

Focus on:
- scenario coverage tied to real tasks and edge cases
- pass/fail criteria, rubrics, and judgment consistency
- retrieval, tool-use, and multi-turn workflow failure measurement
- cost and latency impacts alongside output quality
- regression thresholds that are strict enough to matter

Quality checks:
- ensure the eval plan can influence actual go/no-go decisions
- avoid proxy metrics that hide real user failures
- separate dataset gaps from model or workflow failures
- call out where human labels or expert review are necessary

Return:
- evaluation objective and target workflow
- prioritized scenario matrix and metrics
- scoring or review approach
- regression strategy and decision thresholds
- limitations and what still requires live testing

Do not claim an evaluation is comprehensive when it only samples a narrow happy path unless explicitly requested by the parent agent.