context-driven-development

What This Skill Does

The context-driven-development skill transforms your development environment into a self-documenting ecosystem. By enforcing a structured set of Markdown artifacts—specifically product.md, tech-stack.md, workflow.md, and tracks.md—it forces a clear separation between project intent, technical constraints, and operational processes. When integrated into the OpenClaw agent, these documents serve as the 'long-term memory' for the AI, ensuring that every code suggestion, refactoring task, or architectural decision aligns perfectly with the established project goals rather than drifting into generic or irrelevant solutions.

Installation

To integrate this skill into your project, ensure you have the OpenClaw CLI configured in your shell and execute the following command in your terminal:

npx clawhub@latest install context-driven-development

This will generate the initial templates in your project root, creating a standardized directory structure for your context artifacts.

Use Cases

This skill is highly recommended for:

• Large-Scale Projects: Managing complex, long-running codebases where maintaining consistent architecture is difficult for both human and AI contributors.
• Distributed Teams: Establishing a single source of truth that keeps all developers—and their AI assistants—on the same page regarding the tech stack and implementation rules.
• Standardization: Ensuring that every AI-assisted session begins with a mandatory context review, preventing the 'hallucination' of dependencies or design patterns that fall outside the defined tech stack.

Example Prompts

1. "Analyze our tech-stack.md and suggest a migration path for our outdated authentication library to a secure alternative that fits our existing framework constraints."
2. "Draft a new task in tracks.md based on the current product roadmap in product.md, focusing on the upcoming user authentication feature."
3. "Review the current coding standards outlined in workflow.md and check if the latest pull request complies with our established linting and testing requirements."

Tips & Limitations

• Living Documentation: The primary pitfall is allowing these files to become stale. Treat your context docs like code: include updates in your pull requests.
• Precision: Be as specific as possible in tech-stack.md; the more granular your version definitions, the fewer dependency errors your AI assistant will encounter.
• Limitations: This skill is not intended for single-file scripts or transient experiments where the administrative overhead of maintaining four distinct files would exceed the complexity of the project itself.