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.