What problem does it solve?
Building software with AI agents often fails because long task chains multiply small errors into broken results, token costs spiral, and agents resist or drift from requirements. This Skill provides a complete methodology for AI-native product development that keeps quality verifiable and costs under control.
Core Features & Use Cases
- Document-Driven Development (SDD): Establishes a documentation hierarchy from PRD to technical specs before any code is written, with rules for keeping docs and code in sync.
- Quality Gates and Eval Patterns: Defines action-eval pairs, phase gates, and acceptance thresholds so every agent output is verified, auto-repaired, or escalated to a human.
- Single-Agent-First Execution: Provides decision rules for when to use one agent versus parallel subagents, plus strategies for context compaction and model selection.
- Token Cost Optimization: Explains how strict quality gates raise cache hit rates, counterintuitively lowering total cost.
- Use Case: You want to build a CRUD admin tool with Claude Code from requirements to passing tests. The Skill walks you through writing the PRD and architecture docs first, defining evals per task, then letting the agent execute sequentially through quality gates.
Quick Start
Use the keqian-method skill to design a document-driven development workflow with quality gates for my new AI-assisted project.
