Project Context Scanner
Initializes project architecture graphs, detects build commands, and loads verified codebase context for AI coding sessions.
Project Context Scanner serves as the foundational initialization step for AI agent workflows within the Supergraph ecosystem. It automatically detects project language configurations, verifies build, lint, and test toolchains, and interfaces with codebase-memory-mcp and Serena to build and cache intelligent architectural graphs. By persisting environment details and symbol mappings into a session file with built-in TTL caching, it ensures subsequent AI development steps have precise, high-fidelity context without redundant re-indexing.
Key Features
01Graph architecture analysis powered by codebase-memory-mcp with intelligent 10-minute TTL cache reuse
02Symbol overview mapping and project activation via Serena integration
03Automated generation and synchronization of .supergraph-env session configurations
04Liveness and integrity checks to prevent degraded graph contexts from entering agent workflows
0523 GitHub stars
06Automatic detection of project types and command toolchains across Node, Rust, Python, Go, and Flutter
Use Cases
01Bootstrapping unified test, lint, and build command discovery for AI-assisted workflows
02Switching branches and automatically verifying or refreshing cached repository graph indexes
03Starting a new Claude Code development session with full architecture graph awareness