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Analyze codebases and generate AI-ready configs for faster project customization.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Project Customization MCP" yet — see the docs or source repo.
Analyze this existing codebase’s tech stack, folder structure, and key conventions, then generate a set of AI-ready project configuration files covering code style, common commands, testing workflow, and important caveats.
A set of AI-oriented config files and a summary of project conventions for future development assistance.
Based on the current repository, generate project customization configs aligned with industry best practices, defining development workflow, dependency management, branching conventions, linting, and test execution for both the team and AI tools.
Structured configuration files and guideline notes that help the team and AI collaborate under shared standards.
After scanning the project code, produce AI-ready project documentation configs that help new contributors understand the project structure, key modules, run commands, development constraints, and best practices.
Onboarding-friendly configs and summary documentation that reduce the learning curve for taking over the project.
Build, debug, and manage software tasks with natural language across LLMs.
Analyze project architecture and detect similar code patterns for cross-language consistency.
Query an AI project portfolio, search by tech, and get project details.
Read and manage OpenProject data with guarded tools and confirmed write actions.
Store, search, and summarize codebase notes during coding sessions.
Manage OpenProject projects, work packages, activities, and wiki pages through AI.