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eval-harness-ops

Plan evaluation datasets, rubrics, thresholds, and regression gates for AI agent changes.

1|Updated Mar 12, 2026
Search Tags:#metrics#evaluation#rubrics#quality-gates#datasets#go-no-go
One-click install
npx skills add https://github.com/XiaoPuOuO/VFactory --skill eval-harness-ops
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eval-harness-ops
Source: https://github.com/XiaoPuOuO/VFactory/tree/main/paperclip-official/AgentSetting/skills/eval-harness-ops
Command: npx skills add https://github.com/XiaoPuOuO/VFactory --skill eval-harness-ops

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Organizations shipping AI agents need a formal, reproducible evaluation process that gates changes with measurable quality criteria. This Skill provides a structured plan to design datasets, rubrics, thresholds, regression checks, and reporting to ensure safe deployments.

Core Features & Use Cases

  • Dataset curation: Build representative evaluation datasets with segmentation by user role and risk tier.
  • Rubric design: Create scoring schemes that multiple reviewers can apply consistently.
  • Thresholds & gates: Define pass/fail criteria and regression gates to prevent silent degradation.
  • Reporting: Generate actionable results and a final go/no-go recommendation for release decisions.

Quick Start

Design and run an evaluation plan that measures policy compliance, dataset quality, and go/no-go criteria.

Frequently Asked Questions about eval-harness-ops

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

FAQPage Schema
How do I set up quality gates for AI agent evaluations?▼

Designing AI agent evaluation rubrics involves creating scoring schemes that multiple reviewers can apply consistently. You build representative datasets segmented by user role and risk tier to measure policy compliance and ensure reproducible evaluation results.

What is the best way to decide whether to release an AI agent update?▼

Running AI agent regression checks involves identifying changes to be evaluated and applying pass/fail criteria to impacted workflows. You apply the evaluation scope to datasets and thresholds to prevent silent degradation before generating actionable reporting.

How do I create evaluation datasets for AI agents with different risk tiers?▼

AI agent evaluation reports must contain explicit metrics with defined pass/fail criteria and a final go/no-go recommendation with rationale. This structured reporting ensures actionable results for deployment decisions like merge, release, or phased rollout.

When do I need a formal evaluation plan for AI agent deployments?▼

Yes, multiple reviewers can apply AI agent evaluation rubrics consistently when you design structured scoring schemes. The evaluation plan ensures reproducible results by applying defined thresholds and regression gates to curated datasets.

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