TypingMindTypingMindThe best frontend for AI
Dcc Diagnostics logo

Dcc Diagnostics

Organization
dcc-mcp
dcc-diagnostics

Infrastructure skill — DCC-agnostic observability primitives: generate error reports, 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. Call dcc_diagnostics__error_report first whenever a tool fails with a vague error message. Not for primary task execution — use a domain skill for actual DCC operations.

Overview

Publisherdcc-mcp
Repositorydcc-mcp-core
Skill namedcc-diagnostics
Stars
74
Forks
7
Bundled files
6
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by dcc-mcp on GitHub. Read the source before you install it.

Installation

Install the Dcc Diagnostics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/dcc-mcp/dcc-mcp-core/tree/main/python/dcc_mcp_core/skills/dcc-diagnostics
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dcc-mcp/dcc-mcp-core.git /tmp/dcc-mcp-core
mkdir -p .claude/skills
cp -r /tmp/dcc-mcp-core/python/dcc_mcp_core/skills/dcc-diagnostics .claude/skills/dcc-diagnostics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dcc Diagnostics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dcc Diagnostics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dcc Diagnostics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DCC Diagnostics

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.

Recommended debugging workflow

When a DCC tool fails with a vague error, call tools in this order:

1. dcc_diagnostics__error_report   ← start here: log lines + failed jobs + env
2. dcc_diagnostics__audit_log      ← sandbox-level denials and recent invocations
3. dcc_diagnostics__tool_metrics   ← identify consistently slow or failing tools
4. dcc_diagnostics__screenshot     ← capture current visual state for confirmation
5. dcc_diagnostics__process_status ← check if the DCC process is still alive

Tools

dcc_diagnostics__error_report

Start here when any tool fails. Collects a single-response diagnostic bundle:

  • Log tail: last N lines of dcc-mcp-<dcc>.*.log, extracting ERROR/WARNING lines
  • Failed jobs: recent failed/interrupted entries from the SQLite job-persistence DB
  • Process snapshot: PID, platform, Python version, active DCC_MCP_* env vars
  • Diagnosis hints: actionable text explaining what is wrong and how to fix it

Requires DccServerBase(enable_file_logging=True, enable_job_persistence=True). Both are on by default since dcc-mcp-core v0.14.6.

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 display: DXGI Desktop Duplication API
  • Windows window: Windows.Graphics.Capture
  • Linux: X11 XShmGetImage
  • Fallback: Mock synthetic backend (headless/CI)

Successful DCC-window results include window_handle, window_title, capture_backend and capture_health. Treat capture_health=unverified as pixels without semantic content validation and unusable_capture and desktop_unavailable are structured failures; do not retry them through another desktop or input path.

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

python
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__error_report and all other tools are now available

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dcc Diagnostics AI skill do?

Infrastructure skill — DCC-agnostic observability primitives: generate error reports, 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. Call dcc_diagnostics__error_report first whenever a tool fails with a vague error message. Not for primary task execution — use a domain skill for actual DCC operations.

Why use Dcc Diagnostics on TypingMind?

Because you install it once and use it with any model. Dcc Diagnostics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dcc Diagnostics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dcc-mcp/dcc-mcp-core/tree/main/python/dcc_mcp_core/skills/dcc-diagnostics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dcc Diagnostics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dcc Diagnostics?

As many as you like. As long as a model supports skills, you can use Dcc Diagnostics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dcc Diagnostics AI skill free?

Yes. It is published on GitHub by dcc-mcp under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Related AI Skills

View all
openclaw logo

Control Ui E2e

openclaw
OrganizationPopular

Use when designing, testing, fixing, or extending the OpenClaw Control UI GUI, including UI stress-test galleries with feedback inputs, Vitest + Playwright end-to-end checks, mocked Gateway flows,…

82.2K
391K
1 files
View
openclaw logo

Deslop

openclaw
OrganizationPopular

Diff-scoped AI-slop cleanup pass: strip comment slop, defensive-check slop, type-laundering, and style drift from the current branch diff before autoreview.

82.2K
391K
Instructions
View
openclaw logo

Discord Clawd

openclaw
OrganizationPopular

Use to talk to the Discord-backed OpenClaw agent/session; not for archive search.

82.2K
391K
1 files
View
openclaw logo

Discrawl

openclaw
OrganizationPopular

Discord archive: search, sync freshness, DMs, summaries, TUI, repo/release work.

82.2K
391K
1 files
View

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇