Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
日本語の概要は準備中です。原文の説明を表示しています。
Use when checking capabilities or instructing the user on how to interact with specialized MCP servers like Unreal, Rhino, DaVinci, or TouchDesigner.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
You are the authoritative skill for managing and utilizing the specialized Model Context Protocol (MCP) servers installed in this environment. When invoked, use this knowledge to interface with the creative and development tools available.
github (@modelcontextprotocol/server-github)
chrome-devtools (@modelcontextprotocol/server-puppeteer)
bdb_unreal_mcp (Unreal Engine 5 C++/TS Bridge)
ChiR24/Unreal_mcp including the native C++ McpAutomationBridge plugin.bdb_rhino_mcp & bdb_rhino_mcp_fallback (Rhino 3D MCP Servers)
RhinoMCP community server and GOLEM 3DMCP Python fallback with 105 tools. full NURBS, layers, Grasshopper, and rendering.bdb_davinci_mcp & bdb_davinci_mcp_fallback (DaVinci Resolve MCP Servers)
davinci-mcp-professional fallback. Automation of timelines, media pool, metadata, and color grading.bdb_grandma3_mcp (grandMA3 console)
bdb_resolume_mcp (Resolume Arena REST)
adobe_mcp & adobe_uxp_mcp (Adobe UXP/Scripting)
bdb_blender_mcp & bdb_blender_mcp_fallback (Blender MCP Servers)
BlenderMCP and djeada python server fallback. Scene inspection, material control, object manipulation, rendering.bdb_after_effects_mcp & bdb_after_effects_mcp_fallback (After Effects MCP Servers)
bdb_vectorworks_mcp (Vectorworks CAD/BIM)
bdb_td_minddesigner & bdb_td_backup (TouchDesigner MCP Servers)
zavora_computer_use (Zavora Computer Use)
bdb_grandma3_mcp or bdb_resolume_mcp.bdb_rhino_mcp, bdb_unreal_mcp, or bdb_blender_mcp.bdb_td_minddesigner as your primary TouchDesigner controller, falling back to bdb_td_backup for direct parameter modifications.bdb_after_effects_mcp and adobe_uxp_mcp for lightning-fast performance, falling back to their python or Go+ script engines.mcp_config.json configuration and ensure the respective host applications are running with API/OSC/Lua/network plugins enabled.mcp-manage is the authoritative router for all specialized BDB Model Context Protocol (MCP) servers, ensuring the AI agent selects the right tool for Unreal, Rhino, DaVinci, and others.
| Rationalization | Reality |
|---|---|
| "I can just write a Python script and run it externally." | MCP tools provide direct WebSocket/native integration; external scripts lose context and safety bounds. |
| "I'll use the fallback server first since it's simpler." | Always use the primary optimized MCP server (e.g., bdb_td_minddesigner) unless it fails. |
| "The server isn't responding, I'll just give up." | You must verify if the host app (e.g., Unreal Engine) is actually open and running before failing. |
bdb_rhino_mcp) exists.mcp_config.json when a server connection fails.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
日本語の概要は準備中です。原文の説明を表示しています。
Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.
日本語の概要は準備中です。原文の説明を表示しています。
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
日本語の概要は準備中です。原文の説明を表示しています。
Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
日本語の概要は準備中です。原文の説明を表示しています。
agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
日本語の概要は準備中です。原文の説明を表示しています。