ai-sdlc-operating-model

What problem does it solve?

This Skill turns chaotic AI-assisted development into a clear operating model, defining who does what, when decisions must pause for approval, and what evidence each stage must leave behind.

Core Features & Use Cases

• End-to-end workflow design: Maps the lifecycle from context and classification through implementation, validation, review, merge, close, and learn.
• Authority and gate definition: Assigns the decision holder at each stage and specifies the exit gate that must pass before work can continue.
• Gap analysis against reality: Compares the intended operating model with observed practice in recent PRs, incidents, and repo policies to identify missing controls.
• Governance composition: Connects supporting discipline skills such as startup context checks, change classification, approval boundaries, diff discipline, closeout reporting, memory governance, and compliance audit.
• Use case: A team adopting coding agents can use this Skill to document how agents and humans should collaborate safely, including merge authority, escalation paths, and evidence requirements.

Quick Start

Ask for a repo-grounded AI-SDLC operating model that defines stages, authority, gates, evidence, gaps, and an incremental adoption plan.