---

name: service-integrations description: "Operate M-flow as a FastAPI service, web UI, MCP server, Docker Compose stack, authenticated multi-tenant backend, cloud sync/distributed worker, or face-aware playground integration." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

service-integrations

Use this sub-skill for M-flow service surfaces: API startup, Docker Compose, the web UI, MCP transport and API mode, auth and settings, cloud sync, distributed workers, and the face-aware playground.

Route elsewhere

• In-process Python calls such as m_flow.add(), memorize(), search(), query(), ingest(), or learn() → ../core-memory-api/
• Storage backend and retrieval tuning details → ../retrieval-graph-search/
• Loaders, chunking, content routing, and pipeline internals → ../ingestion-pipelines/

Use this sub-skill when

• starting or checking m_flow.api.client:app
• launching mflow -ui or m_flow.api.v1.ui.start_ui()
• wiring m_flow-frontend, m_flow-mcp, Docker Compose, or the playground stack
• configuring auth, multi-tenancy, settings, or health checks
• using MCP stdio/SSE/HTTP, remote API mode, or task tracking
• enabling cloud sync or Modal workers
• connecting face-aware playground sessions to fanjing-face-recognition

Bundled files

• deployment-and-integrations.md
• api-server-reference.md
• mcp-reference.md
• troubleshooting.md
• service_status_check.py
• mcp_tool_summary.py

Guardrails

• Prefer safe status checks over service mutation.
• Treat face-recognition playground setup as distilled guidance only; it downloads models and needs camera access, so do not run automated installers unless the user explicitly asks.
• Do not route core memory operations here; use the core-memory API sub-skill instead.
• Keep all runtime links inside this sub-skill tree or to sibling generated skill files.