Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Utilizes the Resolume Arena MCP server to trigger clips, clear layers, adjust speeds, and query composition status via the Resolume REST API.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
This skill file instructs AI agents on how to interface with Resolume Arena media servers using the resolume_mcp.py script. It details REST endpoint interactions, layer clearing, composition queries, and troubleshooting steps.
The Resolume Arena MCP Server exposes Resolume Arena's local REST API (7.26+) to AI agents. In the BDB OS media playback pipeline, this enables dynamic clip triggering, layer mixing, composition speed overrides, and status feedback for real-time visual performances.
http://localhost:8080/api/v1.layer=1, clip=1)./composition to retrieve current deck layouts and identify which columns/rows contain clips before firing play triggers.| Tool | Parameters | Description |
|---|---|---|
resolume_ping | None | Verifies if Resolume Arena is running by querying the /product endpoint. |
trigger_clip | layer: int, clip: int | Fires a clip located on a target layer and clip index (1-indexed). |
clear_layer | layer: int | Stops playback and clears visual output on a specific layer (1-indexed). |
get_composition | None | Retrieves a full JSON representation of the active composition, decks, layer routing, and parameter states. |
set_composition_speed | speed: float | Adjusts composition playback speed multiplier. |
Trigger clip 3 on layer 1 and clip 5 on layer 2 simultaneously:
// Step 1: Trigger layer 1 clip
// Tool Call: trigger_clip
{
"layer": 1,
"clip": 3
}
// Step 2: Trigger layer 2 clip
// Tool Call: trigger_clip
{
"layer": 2,
"clip": 5
}
Clear the primary foreground visual layer and scale composition speed to normal:
// Step 1: Clear foreground layer (Layer 3)
// Tool Call: clear_layer
{
"layer": 3
}
// Step 2: Normalize master composition speed
// Tool Call: set_composition_speed
{
"speed": 1.0
}
localhost8080 (Default REST Port)http://localhost:8080/api/v18080. (If you use a password, note that the current MCP server relies on unauthenticated local network access; disable password auth or modify resolume_mcp.py to add auth headers).Resolume API not reachable: HTTP Error 404
Trigger Clip fails / success status returned but no output
get_composition to inspect deck configurations and verify that the target layer/clip slot contains media.Port Conflict on 8080
RESOLUME_API_BASE inside resolume_mcp.py.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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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.
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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.
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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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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.
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