// Work on the Python multi-agent pipeline and MCP client in agentic_ai. Use when editing files under agentic_ai/, changing agent orchestration, configuration loading, MCP client connectivity, async execution flow, pipeline startup commands, or cloud deployment wrappers for the Python service.
Work on Lumina deployment and infrastructure assets. Use when editing terraform/, aws/, docker-compose.yml, DEPLOYMENT.md, ADVANCED_DEPLOYMENTS.md, agentic_ai/deployments/, or other files related to Docker, Terraform, AWS or Azure rollout behavior, environment wiring, and release automation.
Develop and maintain the standalone Lumina MCP server. Use when editing files under mcp_server/, adding or modifying tools, resources, prompts, middleware, configuration, or transport behavior for the Model Context Protocol server.
Implement and debug the Express and TypeScript backend for Lumina. Use when editing files under server/src or server/package.json, changing authentication, conversations, guest flows, chat routes, models, middleware, API contracts, or Gemini and Pinecone service wiring outside the dedicated knowledge-ingestion workflow.
Build, refine, and debug the React/Vite frontend for Lumina. Use when editing files under client/src or client/package.json, changing chat UX, landing page content, auth flows, routing, theme behavior, markdown rendering, animations, responsive layout, or frontend API wiring.
Manage Lumina's retrieval-augmented generation and knowledge ingestion workflow. Use when editing server/src/services/knowledgeBase.ts, server/src/services/pineconeClient.ts, server/src/scripts/knowledgeCli.ts, server/src/models/KnowledgeSource.ts, files under server/knowledge, manifest-based sync inputs, or debugging grounded answers, retrieval relevance, source lifecycle, and inline citations.
| name | lumina-agentic-mcp |
| description | Work on the Python multi-agent pipeline and MCP client in agentic_ai. Use when editing files under agentic_ai/, changing agent orchestration, configuration loading, MCP client connectivity, async execution flow, pipeline startup commands, or cloud deployment wrappers for the Python service. |
Use this skill for the Python subsystem under agentic_ai/. Keep changes consistent with the existing config-driven, async, multi-agent design rather than turning it into a one-off script.
references/file-map.md when you need to locate the pipeline, agent, config, or MCP surface that owns a behavior.references/commands.md before finishing so validation and startup commands match the subsystem.core/, agents/, and mcp_client/ rather than duplicating logic.agentic_ai/__main__.py.agentic_ai/config/.python -m compileall agentic_ai for low-friction validation when dependencies or tests are unavailable.python -m agentic_ai visualize or python -m agentic_ai run --task "..." only when the environment is ready.pytest is runnable just because it appears in requirements.txt; verify the test target exists first.