Infrastructure skill - guide developers and agents through creating or modernizing a full DCC-MCP adapter for Nuke, Blender, 3ds Max, Unreal, ZBrush, Houdini, Maya, and custom studio tools. Use when building server, dispatcher, gateway, packaging, and runtime integration. Not for authoring individual SKILL.md tool packages - use dcc-mcp-skills-creator.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Infrastructure skill - guide developers and agents through creating or modernizing a full DCC-MCP adapter for Nuke, Blender, 3ds Max, Unreal, ZBrush, Houdini, Maya, and custom studio tools. Use when building server, dispatcher, gateway, packaging, and runtime integration. Not for authoring individual SKILL.md tool packages - use dcc-mcp-skills-creator.
Use this skill when you are creating a new DCC-MCP adapter or modernizing an
existing adapter repository: server composition, host-thread dispatch,
sidecar/gateway wiring, readiness, resources, project state, diagnostics,
install lifecycle, or cross-DCC verification.
For individual skill packages (SKILL.md, tools.yaml, scripts, groups, and
skill taxonomy), load dcc-mcp-skills-creator instead.
Fast Workflow
Classify the host integration:
Embedded Python host: Blender, 3ds Max Python, Houdini, Maya, Nuke.
Use core helpers for skill discovery, MinimalModeConfig, project tools, resources, diagnostics, context snapshots, install lifecycle, and gateway failover before writing adapter-local wrappers.
Choose the dcc-mcp-server run mode deliberately: no subcommand or auto for backwards-compatible first-wins auto-gateway, serve --no-auto-gateway when a separate daemon owns the gateway port, and gateway for a machine-wide daemon with no inline DCC execution.
If the adapter cannot share the gateway FileRegistry, register remotely through POST /v1/instances/register, refresh with /heartbeat, and deregister on shutdown; the gateway will expose the row as source: "http" in gateway://instances / GET /v1/instances, preserve instance_short and mcp_url, and route it through the same live_instances contract.
For same-LAN convenience discovery, build with mdns and pair adapter-side --advertise-mdns with gateway-side ; treat this as a multicast discovery hint only, keep auth/TLS policy explicit, and prefer HTTP registration or relay for routed/subnet-crossing production deployments.
Example: New Nuke Adapter
When asked to create a Nuke MCP adapter, start by mapping the host lifecycle:
how Python is loaded, how the UI/main thread must be entered, what headless
mode is available, how plugins are installed, and which operations should be
bundled as default skills. Then scaffold the adapter around core primitives:
DccServerBase for MCP/HTTP and skill catalog behavior.
DccServerOptions.from_env("NUKE") or an adapter-specific equivalent for env-driven configuration.
HostExecutionBridge plus a Nuke dispatcher for all Nuke API calls.
Core project, readiness, resource, diagnostics, and gateway helpers before adapter-local glue.
dcc-mcp-skills-creator for the first nuke-* skill packages.
Non-Negotiables
Do not touch a DCC API from a Tokio/HTTP worker thread.
Do not parse or rewrite SKILL.md, tools.yaml, groups.yaml, or prompt/workflow files in adapter runtime code when core exposes a typed object or catalog API.
Do not reach into server._server unless no public core API exists; if you must, file a core issue and keep the adapter shim small.
Do not create Maya-only abstractions in shared core or adapter templates.
Do not expose raw script execution as the primary user workflow when a typed skill can cover the task.
Do not publish local paths, private machine names, or source-attribution markers in public issues or PR text.
--discover-mdns
For NAT or routed-subnet deployments, run the tunnel agent with stable instance_id, capabilities_fingerprint, adapter_version, and scene metadata, then configure the standalone gateway with --relay-source ADMIN_URL=PUBLIC_BASE_URL; the gateway will expose active tunnels as source: "relay" rows with relay details in source_meta after probing /v1/healthz through <PUBLIC_BASE_URL>/tunnel/<tunnel_id>/mcp.
Preserve gateway caller attribution when adding adapter wrappers or admin/debug routes: let MCP initialize.params.clientInfo, MCP _meta.agent_context, REST meta.agent_context, x-dcc-mcp-* headers, and safe User-Agent fallbacks flow through core rather than logging raw prompts or local machine data.
For lifecycle/memory/telemetry policy, use register_lifecycle_hooks(...), search_skills(..., session_id=...), dispatch_session_start(...), dispatch_before_tool_call(...), dispatch_after_tool_call(...), and dispatch_session_end(...); pair MemoryRecorder(InMemoryMemoryStore()).install(hooks) with those hooks when adapters need bounded memory summaries, failed-pattern avoidance, or session compaction, and disable the recorder for privacy-sensitive deployments. Open a focused core issue/RFC only when those public hooks cannot express the adapter boundary.
Add one executable smoke path: unit tests for construction plus either headless DCC, mock dispatcher MCP calls, gateway REST replay, mDNS same-LAN discovery smoke, relay-source smoke, or just idle-memory-smoke for standalone server idle/regression checks.
For gateway/admin observability, surface explicit state instead of silent zeroes: traffic panels should report disabled, unavailable, filtered, or genuine no-traffic states; skill panels should distinguish discovered, loaded, searched, selected, called, failed, and low-adoption skills; and admin-facing frames/paths should stay metadata-only or aliased unless an operator explicitly configures a private raw sink.
Preserve workflow observability: adapter calls should carry request, parent, trace, session, DCC, transport, and artifact/validation metadata so the Admin workflow graph can show Intent → Discovery → Skill Load → Tool Calls → Fallbacks → Artifacts → Validation → Report without raw log reading.
Preserve bounded agent_context task/session/turn metadata and artifact/validation-friendly tool names so Admin task outcomes can group workflows, calls, deliverables, and checks without reading raw payloads or local paths.
