dev-intelligence-orchestrator

To automate static analysis for a C++ and Python monorepo, you can run scripts that orchestrate cppcheck and pylint. The tool detects project types automatically and applies learned configurations to analyze code and return structured JSON results.

A self-learning loop improves dev tool orchestration by querying an MCP prompts server before, during, and after tool runs. It captures successful configurations as new prompts, allowing future runs to reuse optimized settings automatically.

Yes, you need an MCP Prompts server running to execute automated project analysis. The orchestration queries this server to retrieve and store learned configurations, and requires a workspace with project code to function properly.

Yes, you can use PlatformIO and pytest within an automated dev-ops workflow. The orchestration integrates these tools to handle testing across detected project types, applying configurations refined through the self-improving learning loop.

The best way to configure cppcheck and pylint for CI is using an intelligent orchestrator that captures successful tool configurations as reusable prompts. This reduces manual context switching and automatically optimizes build and test workflows.

Intelligent project detection matters for automated code analysis because it identifies languages, frameworks, and project nature to tailor tooling. This ensures the correct tools and learned configurations are applied automatically for accurate analysis results.