unity-mcp-orchestrator

AI Community
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Orchestrate Unity Editor via MCP (Model Context Protocol) tools and resources. Use when working with Unity projects through MCP…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Template Notice”, “Quick Start: Resource-First Workflow”, “Critical Best Practices”, “1. After Writing/Editing Scripts: Wait for Compilation and Check Console”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name unity-mcp-orchestrator directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Template Notice / Quick Start: Resource-First Workflow / Critical Best Practices” before expanding.

Boundaries And Review

  • Dependencies: It usually needs no extra API key, so start with a small validation task.
  • Permissions: Declared permissions include read / write / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.
Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates
Fluxly category
AI
Author-declared agents
No explicit declaration found; this is not inferred or tested compatibility
Static check
88 / 100 · heuristic scan, not runtime safety proof
Author / version / license
@CoplayDev · no license declared
Fluxly token estimate
Moderate
Fluxly setup estimate
Guided setup
External API key
No requirement detected
Detected OS requirements
Unspecified
Runtime requirements
Python
Detected file/system behavior
  • Read-only
  • Write / modify
  • Shell exec
Detected network behavior
External requests
Install commands
None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools,默认拥有全部工具权限。

Output preview unity-mcp-orchestrator.preview
# 3. Use Screenshots to Verify Visual Results

# Basic screenshot (saves to Assets/, returns file path only)
manage_camera(action="screenshot")

# Inline screenshot (returns base64 PNG directly to the AI)
manage_camera(action="screenshot", include_image=True)

# Use a specific camera and cap resolution for smaller payloads
manage_camera(action="screenshot", camera="MainCamera", include_image=True, max_resolution=512)

# Batch surround: captures front/back/left/right/top/bird_eye around the scene
manage_camera(action="screenshot", batch="surround", max_resolution=256)

# Batch surround centered on a specific object
manage_camera(action="screenshot", batch="surround", view_target="Player", max_resolution=256)

# Positioned screenshot: place a temp camera and capture in one call
manage_camera(action="screenshot", view_target="Player", view_position=[0, 10, -10], max_resolution=512)

# Scene View screenshot: capture what the developer sees in the editor
manage_camera(action="screenshot", capture_source="scene_view", include_image=True)

# Scene View framed on a specific object
manage_camera(action="screenshot", capture_source="scene_view", view_target="Canvas", include_image=True)

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