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lumina-agentic-mcp

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UpdatedMarch 18, 2026 at 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

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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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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.