Verification

Organization

dcc-mcp

verification

Cross-DCC verification toolset - deterministic capture review views, in-band image statistics, scene-vs-spec validation, and reference-vs-render comparison sheets. Pure Python, read-only, DCC-agnostic. Use to close the quality loop without shipping pixels to a model: flag white/near-black/gamma-broken frames from stats alone and catch missing-UV/missing-part exports before export. Not for scoring or aesthetic review - this toolset packages evidence and computes no scores.

Overview

Publisher	dcc-mcp
Repository	`dcc-mcp-core`
Skill name	`verification`
Stars	74
Forks	7
Bundled files	6
License	MIT
Links	Skill source Repository Homepage

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/dcc-mcp/dcc-mcp-core/tree/main/python/dcc_mcp_core/skills/verification
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/dcc-mcp/dcc-mcp-core/tree/main/python/dcc_mcp_core/skills/verification

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/verification .claude/skills/verification

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verification 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 Verification 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 Verification 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.

Verification

A cross-DCC, read-only verification toolset (affinity: any) that closes the capture-and-look quality loop with deterministic, machine-checkable signals. Inspired by img2threejs's "deterministic-first, model-last" review design, it computes evidence in-band — statistics, hashes, and verdicts — and never ships pixels to a model unless a caller explicitly asks for image/both payloads.

The four tools map to the four legs of the deterministic capture contract:

capture_review_views — the fixed front / side / top / three-quarter view plan, viewport-only, fails loudly on a missing, wrong-size, or blank frame.
image_stats — mean luma, stddev, histogram, uniformity, and bad-frame flags (white playblast, near-black render, missing display transform).
validate_scene_vs_spec — hierarchy names, material bindings, per-mesh UV coverage, part existence, non-manifold, and Euler checks, before export.
make_comparison_sheet — reference + renders side-by-side into one image; packages evidence and computes no scores.

Pixel-level metrics (pHash / dHash / aHash, silhouette IoU, SSIM, CIEDE2000, Sobel edge maps) live in dcc_mcp_core.verification as deterministic library functions, not skill tools, so callers gate on them directly.

Safety Contract

Every tool is read-only and side-effect free except make_comparison_sheet, which writes exactly one output image at the requested path. Tools never mutate the input captures and never launch a DCC. PPM/PGM inputs decode through the standard library; other formats (PNG/JPEG/EXR) decode through ffmpeg on PATH (or vx ffmpeg) and fail loudly when the decoder is unavailable.

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 Verification AI skill do?

Why use Verification on TypingMind?

Because you install it once and use it with any model. Verification 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 Verification 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/verification. 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 Verification?

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 Verification?

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

Is the Verification 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.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Verification

Safety Contract

Bundled files

Frequently asked questions

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