| 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.
