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lumina-agentic-mcp

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Atualizado18 de março de 2026 às 16:59

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.

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hoangsonww

hoangsonww/AI-RAG-Assistant-Chatbot

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Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Desenvolvedores de softwareInformática e Matemática·SOC 15-1252

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4 arquivos

SKILL.md

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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.

Lumina Agentic Mcp

Overview

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.

Load The Right Reference

Read references/file-map.md when you need to locate the pipeline, agent, config, or MCP surface that owns a behavior.
Read references/commands.md before finishing so validation and startup commands match the subsystem.

Preserve The Existing Architecture

Keep pipeline behavior flowing through core/, agents/, and mcp_client/ rather than duplicating logic.
Respect async boundaries and existing command entry points in agentic_ai/__main__.py.
Keep the MCP server contract explicit when changing tool names, prompt names, or resource URIs.
Preserve configuration-driven behavior through files in agentic_ai/config/.

Validate With What The Repo Actually Supports

Prefer python -m compileall agentic_ai for low-friction validation when dependencies or tests are unavailable.
Use targeted CLI smoke checks such as python -m agentic_ai visualize or python -m agentic_ai run --task "..." only when the environment is ready.
Do not assume pytest is runnable just because it appears in requirements.txt; verify the test target exists first.

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lumina-infra-deploy

hoangsonww/AI-RAG-Assistant-Chatbot

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.

2026-03-3141

lumina-mcp-server

hoangsonww/AI-RAG-Assistant-Chatbot

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.

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lumina-backend-api

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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.

2026-02-2841

lumina-frontend-ui

hoangsonww/AI-RAG-Assistant-Chatbot

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.

2026-02-2841

lumina-rag-knowledge

hoangsonww/AI-RAG-Assistant-Chatbot

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.

2026-02-2841

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.

Lumina Agentic Mcp

Overview

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.

Load The Right Reference

Read references/file-map.md when you need to locate the pipeline, agent, config, or MCP surface that owns a behavior.
Read references/commands.md before finishing so validation and startup commands match the subsystem.

Preserve The Existing Architecture

Keep pipeline behavior flowing through core/, agents/, and mcp_client/ rather than duplicating logic.
Respect async boundaries and existing command entry points in agentic_ai/__main__.py.
Keep the MCP server contract explicit when changing tool names, prompt names, or resource URIs.
Preserve configuration-driven behavior through files in agentic_ai/config/.

Validate With What The Repo Actually Supports

Prefer python -m compileall agentic_ai for low-friction validation when dependencies or tests are unavailable.
Use targeted CLI smoke checks such as python -m agentic_ai visualize or python -m agentic_ai run --task "..." only when the environment is ready.
Do not assume pytest is runnable just because it appears in requirements.txt; verify the test target exists first.