What is Context-Driven Development?

Context-Driven Development (CDD) is a methodology that treats project context as a managed artifact, ensuring AI tools and developers work from a consistent, persistent shared foundation.

Can I use this with existing projects?

Yes, the skill includes a brownfield setup mode that analyzes existing code, configurations, and documentation to automatically pre-populate your context artifacts.

How do I prevent documentation from becoming stale?

The skill enforces a workflow where context artifacts are updated as part of the completion process for every work track, ensuring the docs evolve with the code.

How does this skill improve AI performance?

By providing structured files like product.md and tech-stack.md, the AI has a persistent source of truth, reducing the need for repetitive context-setting prompts and decreasing hallucinations.

What are 'tracks' in the Conductor system?

Tracks are individual units of work that move through specification, planning, and implementation phases, tracked in a central registry to maintain session continuity.

Context-Driven Development

Name: Context-Driven Development
Author: wshobson

bywshobson

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25,583

Productivity & Workflow

Manages structured project context through living documentation to ensure consistent AI interactions and team alignment.

About

The Context-Driven Development (CDD) skill for Claude Code implements a methodology where project documentation is treated as a first-class artifact managed alongside source code. By maintaining a structured foundation of artifacts—including product vision, technology stacks, and development workflows—this skill ensures that AI interactions remain consistent across sessions while providing a single source of truth for human teams. It facilitates a robust 'Context → Spec & Plan → Implement' lifecycle, reducing AI hallucinations and minimizing repetitive prompting in complex projects.

Key Features

Built-in context validation checklists to ensure alignment before implementation
Support for brownfield project discovery to extract context from existing codebases
Registry-based task management via tracks.md for multi-session work units
Automated initialization of standardized context artifacts like product.md and tech-stack.md
25,583 GitHub stars
Standardized templates for product guidelines, code styleguides, and technical specifications

Use Cases

Onboarding developers and AI agents to complex codebases with structured documentation
Establishing rigorous quality gates and workflow patterns for AI-assisted coding
Maintaining high AI accuracy during long-running or multi-session development projects

About

Key Features

Built-in context validation checklists to ensure alignment before implementation
Support for brownfield project discovery to extract context from existing codebases
Registry-based task management via tracks.md for multi-session work units
Automated initialization of standardized context artifacts like product.md and tech-stack.md
25,583 GitHub stars
Standardized templates for product guidelines, code styleguides, and technical specifications

Use Cases

Onboarding developers and AI agents to complex codebases with structured documentation
Establishing rigorous quality gates and workflow patterns for AI-assisted coding
Maintaining high AI accuracy during long-running or multi-session development projects