eval-ops

Designs scorecards, regression reviews, and release gates for prompt and agent evaluation.

Updated May 22, 2026
One-click install
npx skills add https://github.com/handaeho/Harness_Engineering --skill eval-ops-handaeho
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: eval-ops
Source: https://github.com/handaeho/Harness_Engineering/tree/main/prompt-stack/_candidates/v36_candidate/codex/skills/eval-ops
Command: npx skills add https://github.com/handaeho/Harness_Engineering --skill eval-ops-handaeho

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Teams shipping prompts, agents, and workflows often lack a disciplined way to compare candidates, detect regressions, and decide release readiness, leading to overstated claims and unsafe promotions. ## Core Features & Use Cases - Evaluation Design: Defines evaluation surfaces, metric taxonomies, scorecards, and rubrics tied to actual decisions and gates. - Regression & Drift Review: Compares baseline vs candidate under comparable conditions, detects semantic drift, coverage regression, and anomalies. - Release Gating: Enforces gates with owner, threshold, and action, including harness-grade labeling from harness-designed to production-monitored. - Use Case: When reviewing a new coding prompt package, run the required case mix (features, bug fixes, security, prompt injection), keep executed-vs-unexecuted status explicit, and produce a pass/hold/reject verdict with evidence. ## Quick Start Ask the agent to evaluate whether the attached candidate prompt package is release-ready compared to the current baseline, with a scorecard and gate recommendation.

Frequently Asked Questions about eval-ops

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate whether a prompt candidate is release-ready?

Define the evaluation surface, contract, and gate first, then compare the candidate against a stable baseline on the same cohort. A release-ready verdict requires an owner, threshold, action, rollback condition, and executed evidence rather than prose summaries.

What metrics should I use for agent evaluation?

Use grouped metrics such as effectiveness, reliability, safety, cost, and lifecycle fidelity rather than one vague score. Include task success, contract adherence, unsupported-claim rate, and guardrail-trigger correctness, and drop any metric no decision depends on.

How do I detect regression between two prompt versions?

Compare baseline and candidate under comparable conditions on the same evaluation surface, checking task success, contract adherence, latency, safety, and semantic drift. Treat safety regressions and small critical-cohort failures as blockers even when averages improve.

When should I use LLM-as-a-judge instead of deterministic checks?

Use judge-based evaluation only for open-ended, contract-bound outputs where exact-match metrics fail, with explicit criteria and scoring anchors. Judge scores can never override deterministic failures, critical failures, or missing evidence.

What is the minimum evaluation mix for a coding prompt package?

Cover at least simple and complex features, bug fixes, test writing, code review, security detection, ambiguous requirements, prompt injection, API freshness, and over-broad change pressure. Each case needs expected behavior, scoring criteria, and explicit executed-vs-unexecuted status.

When should I not use this evaluation skill?

Skip it for trivial one-shot answers, simple local verification steps, or exploratory work where no monitoring or comparison decision depends on the output. It is designed for evaluation-driven decisions, not general-purpose tasks.