Skip to content

CLI Context Weaver: Hyperlinked Knowledge Graphs for AI Deployments

image

AI coding assistants hallucinate because they lack deep context about your infrastructure. Developers waste time re-explaining architecture, API contracts, and deployment patterns every time they ask for help.

App Concept

  • CLI tool that crawls your codebase, infrastructure-as-code, wikis, and API schemas
  • Builds hyperlinked knowledge graph connecting code → docs → deployment → monitoring
  • Generates rich context bundles that LLMs can consume for accurate code generation
  • Real-time sync keeps context fresh as your infrastructure evolves
  • Works with any AI assistant (Claude, ChatGPT, Copilot) via universal export format

Core Mechanism

  • Install via brew install context-weaver or npm install -g context-weaver
  • Run context-weaver init to scan repo and detect documentation patterns
  • AI agent identifies relationships: "This Terraform module deploys this API documented here"
  • Visual graph shows context coverage with gaps highlighted
  • Export context bundles: context-weaver export --topic=auth for focused AI sessions
  • Git hooks auto-update graph when code changes
  • Gamification: "Context coverage score" motivates teams to improve documentation
  • Integration with Claude Code, Cursor, and GitHub Copilot

Monetization Strategy

  • Open source core CLI tool for individual developers (freemium growth)
  • Team plan ($99/month): Multi-repo support, shared context graphs, team analytics
  • Enterprise ($999/month): SSO, compliance features, on-prem deployment, priority support
  • Marketplace for domain-specific context templates (Kubernetes, AWS, etc.)
  • Consulting services for large-scale knowledge graph migrations

Viral Growth Angle

  • Developer productivity gains are measurable and shareable ("50% fewer hallucinations!")
  • Open source nature drives GitHub stars and community contributions
  • Blog posts auto-generated from context graphs create SEO flywheel
  • CLI tool naturally spreads through team recommendations
  • Integration with popular AI assistants creates network effects
  • "Context quality badge" for repos drives competitive documentation improvements

Existing projects

  • DevDocs - Documentation browser but no knowledge graph
  • Obsidian - Knowledge graphs but not code-focused
  • Sourcegraph - Code search but limited context extraction
  • Swimm - Code documentation but no AI assistant integration
  • Continue.dev - AI coding assistant with some context features
  • No existing tool combines automated knowledge graph building + hyperlinked context + universal AI assistant export

Evaluation Criteria

  • Emotional Trigger: Be indispensable (makes AI assistants actually useful), limit risk (reduces hallucination-induced bugs), be prescient (positioned for AI-assisted development future)
  • Idea Quality: Rank: 9/10 - Solves critical pain point in AI-assisted development, strong technical moat, viral growth potential through open source
  • Need Category: Integration & Acceptance (seamless integration with existing systems) + Strategic Growth (data-driven culture, long-term AI strategy)
  • Market Size: ~20M developers using AI assistants, targeting 1M power users = $100M TAM at $99/month for teams
  • Build Complexity: High - requires code analysis, documentation parsing, knowledge graph algorithms, LLM integration, but clear technical path
  • Time to MVP: 3-4 months with AI coding agents (basic graph builder, git integration, simple export format)
  • Key Differentiator: Only CLI-native platform combining automated knowledge graph building from multiple sources (code, docs, IaC, APIs) with universal AI assistant integration via hyperlinked context bundles