qa

Assess artifact quality against a predefined standard and return a structured verdict.

5.4k|535|Updated Jan 14, 2026
One-click install
npx skills add https://github.com/Q00/ouroboros --skill qa-q00
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: qa
Source: https://github.com/Q00/ouroboros/tree/main/skills/qa
Command: npx skills add https://github.com/Q00/ouroboros --skill qa-q00

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quickly assesses artifacts against a predefined quality bar and returns a structured verdict with actionable next steps.

Core Features & Use Cases

  • Determine artifact quality across code, docs, tests, and API responses with a single pass.
  • Provide PASS/REVISE/FAIL verdicts and recommended loop actions to guide iterative improvement.
  • Use MCP mode when available or fall back to a deterministic internal judge for environments without MCP.

Quick Start

Run ooo qa [file_path | artifact_text] to evaluate your latest output and receive a structured verdict.

Frequently Asked Questions about qa

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

FAQPage Schema
How do I evaluate code quality against a predefined standard?

You can evaluate document quality by running the assessment tool with your file path or artifact text. It checks the artifact against a predefined quality bar and returns a structured verdict with actionable next steps to guide iterative improvement.

Can I automate API response evaluation without an MCP environment?

You can automate API response evaluation in environments without MCP by using the fallback deterministic internal judge. It requires a defined quality bar, clear artifact specification, and optional seed or reference data to drive deterministic results.

What is the best way to get actionable verdicts for test outputs?

The best way to get actionable verdicts for test outputs is to run a single-pass quality assessment against a predefined standard. This process returns a structured verdict and recommended loop actions to guide iterative improvement.

Does the quality evaluation tool support iterative improvement workflows?

The quality evaluation tool supports iterative improvement workflows by providing recommended loop actions alongside its structured verdicts. It determines artifact quality across code, docs, tests, and API responses in a single pass to guide your next steps.

Do I need reference data to evaluate artifact quality deterministically?

You do not always need reference data to evaluate artifact quality, but providing optional seed or reference data helps drive deterministic results. A defined quality bar and clear artifact specification are required to perform the assessment.