DCC Diagnostics

SkillMonitoring & ops

Infrastructure skill, DCC-agnostic observability primitives: capture screenshots, query audit logs, inspect tool performance metrics, and monitor process health. Works in any DCC environment (Maya, Blender, Houdini, Unreal, etc.) or standalone Python. Use for debugging any skill failure or verifying DCC state. Not for primary task execution, use a domain skill for actual DCC operations.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the DCC Diagnostics skill

What this skill tells your AI

The instructions your AI receives, as published by dcc-mcp/dcc-mcp-core in examples/skills/dcc-diagnostics/SKILL.md and read by ahel’s review.

Cross-DCC observability and debugging tools powered by dcc-mcp-core.

All tools work in any DCC environment (Maya, Blender, Houdini, Unreal, 3ds Max) or standalone Python — no DCC-specific APIs required.

Tools

`dcc_diagnostics__screenshot`

Capture the current screen or a specific window as a PNG/JPEG image. Backed by the dcc_mcp_core.Capturer class which uses:

Windows: DXGI Desktop Duplication API (<16ms per frame)
Linux: X11 XShmGetImage
Fallback: Mock synthetic backend (headless/CI)

`dcc_diagnostics__audit_log`

Query the sandbox audit log from dcc_mcp_core.SandboxContext. Returns recent tool invocations with outcome (success/denied) and timestamps.

`dcc_diagnostics__tool_metrics`

Inspect per-tool performance counters from dcc_mcp_core.ToolRecorder: invocation count, success rate, average latency, P95/P99 percentiles.

`dcc_diagnostics__process_status`

Check process health via dcc_mcp_core.PyProcessMonitor. Lists tracked PIDs and their liveness status.

Usage with any DCC MCP server

import os

os.environ["DCC_MCP_SKILL_PATHS"] = "/path/to/dcc-diagnostics"

from dcc_mcp_maya import start_server  # or dcc_mcp_blender, etc.

handle = start_server(port=8765)
# dcc_diagnostics__screenshot is now available as an MCP tool

Signals

GitHub stars: 48
Forks: 4
Last commit: Oct 2026

Advanced

Item type: skill
Key: dcc-diagnostics
Source: github.com/dcc-mcp/dcc-mcp-core

github.com/dcc-mcp/dcc-mcp-core

DCC Diagnostics

What this skill tells your AI

Tools

dcc_diagnostics__screenshot

dcc_diagnostics__audit_log

dcc_diagnostics__tool_metrics

dcc_diagnostics__process_status