context-driven-developmentTreat project context as a managed artifact alongside code. Use structured context documents (product.md, tech-stack.md, workflow.md) to enable consistent AI interactions and team alignment. Essential for projects using AI-assisted development.
Install via ClawdBot CLI:
clawdbot install wpank/context-driven-developmentTreat project context as a first-class artifact managed alongside code. Instead of relying on ad-hoc prompts or scattered documentation, establish a persistent, structured foundation that informs all AI interactions.
npx clawhub@latest install context-driven-development
Creates and maintains a set of context documents that:
Use for:
Skip for:
Keywords: context, project setup, documentation, AI alignment, team workflow, product vision, tech stack
Context precedes code.
Living documentation.
Single source of truth.
AI alignment.
Context ā Spec & Plan ā Implement
Purpose: Captures product vision, goals, target users, and business context.
Contents:
Update when:
Purpose: Documents technology choices, dependencies, and architectural decisions.
Contents:
Update when:
Purpose: Establishes development practices, quality gates, and team workflows.
Contents:
Update when:
Purpose: Registry of all work units with status and metadata.
Contents:
Update when:
context/
āāā product.md # Product vision and goals
āāā tech-stack.md # Technology choices
āāā workflow.md # Development practices
āāā tracks.md # Work unit registry
āāā styleguides/ # Language-specific conventions
āāā python.md
āāā typescript.md
āāā ...
For new projects, create all artifacts from scratch:
context/product.md:context/tech-stack.md:context/workflow.md:context/tracks.md:For existing codebases, extract context from what exists:
context/tech-stack.md:context/product.md:context/workflow.md:Changes in one artifact should reflect in related documents:
Before adding any new dependency:
Before starting any work:
Before starting implementation, validate:
Product Context:
Technical Context:
Workflow Context:
| Anti-Pattern | Problem | Fix |
|--------------|---------|-----|
| Stale Context | Documents become outdated and misleading | Update context as part of each track's completion |
| Context Sprawl | Information scattered across multiple locations | Use defined artifact structure; resist new document types |
| Implicit Context | Relying on knowledge not captured in artifacts | If referenced repeatedly, add to appropriate artifact |
| Over-Specification | Context so detailed it's impossible to maintain | Keep focused on decisions affecting AI behavior and team alignment |
Team Alignment:
AI Consistency:
Institutional Memory:
Generated Mar 1, 2026
A tech startup uses this skill to establish structured context documents (product.md, tech-stack.md, workflow.md) before coding begins, ensuring AI agents generate consistent, aligned code for their minimum viable product. This accelerates development by providing clear product vision and technical constraints from day one, reducing miscommunication among founders and AI tools.
A large corporation adopts this skill to document an existing brownfield project's tech stack and workflows, enabling AI agents to assist in refactoring or migrating legacy code. By extracting context from current dependencies and practices, teams ensure AI-generated code adheres to enterprise standards and integrates smoothly with older systems.
A distributed software-as-a-service team uses context documents to onboard new developers and AI assistants quickly, providing a single source of truth for product goals, coding conventions, and workflow processes. This reduces ramp-up time and maintains consistency in AI-assisted feature development across different time zones.
An edtech company implements this skill to manage context for a learning platform with multiple contributors, using product.md to define user personas (students, teachers) and workflow.md to standardize testing and deployment. AI agents leverage these documents to generate code that aligns with educational goals and compliance requirements.
Companies offer this skill as part of a premium subscription for AI-assisted development platforms, charging monthly fees for access to enhanced context management features. Revenue is generated through tiered plans that include automated context updates and integration with popular IDEs, targeting teams seeking productivity gains.
Agencies provide consulting services to help organizations adopt Context-Driven Development, offering setup, training, and maintenance for context documents. Revenue comes from project-based fees or retainer contracts, especially for enterprises needing custom workflows and AI alignment strategies.
The skill is released as open source to build community adoption, while revenue is generated through paid support, advanced features, or enterprise licenses. This model attracts developers who value transparency and collaboration, with upsells for priority updates and dedicated assistance.
š¬ Integration Tip
Start by creating product.md first to define core goals, then integrate context checks into your existing development workflow to ensure AI agents reference updated documents before generating code.
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