aidlc-cqo

Enforce quality gates, ratchet baselines, and governance for AI-DLC projects.

Updated Apr 11, 2026
One-click install
npx skills add https://github.com/CornFedKratos/s3-aidlc --skill aidlc-cqo
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: aidlc-cqo
Source: https://github.com/CornFedKratos/s3-aidlc/tree/main/plugins/s3-aidlc/skills/aidlc-cqo
Command: npx skills add https://github.com/CornFedKratos/s3-aidlc --skill aidlc-cqo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The CQO role defines and enforces formal quality gates, risk protocols, and governance for AI-DLC projects so teams ship with confidence and auditable quality.

Core Features & Use Cases

  • Authority over test specifications, quality gates, and ratchet baselines to prevent regression and hidden risks.
  • Protocol-driven workflows for feature start, pre-merge quality checks, and knowledge capture in the KB.
  • Clear sign-off, anti-pattern detection, and handoff routines to ensure traceability across sessions.

Quick Start

Initialize CQO governance by aligning the first feature with a test spec, baseline ratchet, and approval workflow.

Frequently Asked Questions about aidlc-cqo

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

FAQPage Schema
How do I enforce quality gates for AI-driven software development?

Quality gates for AI-driven development are enforced by defining formal test specifications, ratchet baselines, and governance protocols that prevent regressions and ensure auditable sign-off across the project lifecycle.

What is a ratchet baseline in AI-DLC risk management?

A ratchet baseline in AI-DLC risk management establishes a strict quality floor that prevents regression, ensuring hidden risks are caught early and project quality continuously improves without backsliding.

How do I ensure auditability and traceability across AI development sessions?

Auditability and traceability across sessions are ensured through protocol-driven workflows for feature start, pre-merge checks, knowledge capture, clear sign-off, and anti-pattern detection handoff routines.

Can I apply governance protocols from feature spec creation to merge sign-off?

Yes, governance protocols span the entire CQO domain, applying formal quality controls from initial feature spec creation through pre-merge quality checks to final knowledge base documentation.

What's the best way to initialize quality governance for an AI-DLC project?

Initialize quality governance by aligning your first feature with a test specification, establishing a baseline ratchet, and configuring an approval workflow to enforce reliable sign-off.

When do I need formal quality gates and anti-pattern detection for AI development?

Formal quality gates and anti-pattern detection are needed when teams ship AI-DLC projects requiring strict risk management, auditable quality, and reliable traceability across development sessions.