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Setup And Assets

Organization
VectorSpaceLab
setup-and-assets

Prepare 3DDFA_V2 runtime assets and native extension builds before demos or benchmarks.

Overview

PublisherVectorSpaceLab
RepositoryAREX-Skill
Skill namesetup-and-assets
Stars
341
Forks
26
Bundled files
2
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 2 bundled files

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

  • Open source

    Published by VectorSpaceLab on GitHub. Read the source before you install it.

Installation

Install the Setup And Assets 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/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/3ddfa-v2/sub-skills/setup-and-assets
  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/VectorSpaceLab/AREX-Skill.git /tmp/AREX-Skill
mkdir -p .claude/skills
cp -r /tmp/AREX-Skill/skills/repositories/repo-skills/3ddfa-v2/sub-skills/setup-and-assets .claude/skills/setup-and-assets
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Setup And Assets 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 Setup And Assets 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 Setup And Assets 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.

Setup and assets

Use this sub-skill before any 3DDFA_V2 demo or benchmark, and whenever a user reports missing checkpoints, failed imports, or native-build errors.

When to read

Read this sub-skill when the task mentions:

  • build.sh, build_cpu_nms.sh, build_sim3dr.sh, render.so, cpu_nms, or Sim3DR_Cython.
  • Missing checkpoints, BFM files, config YAML files, or ONNX artifacts.
  • Import errors from FaceBoxes, TDDFA, utils.render, Sim3DR, or onnxruntime.
  • NumPy alias errors such as np.long or Cython errors such as np.int_t.

Preparation workflow

  1. Check assets. Run the bundled asset checker against the checkout root. It reads the selected YAML config and reports missing checkpoints, BFM files, and sample inputs.
  2. Build native extensions. Build FaceBoxes NMS, Sim3DR, and render.so in that order. These are needed even for many headless demo paths because the top-level scripts import rendering modules.
  3. Smoke-test imports. Run the core import helper; it loads FaceBoxes, constructs TDDFA from the default config, imports the ONNX classes, and verifies the render import surface.
  4. Only then route onward. Send still-image requests to ../still-image-demo/, video/tracking requests to ../video-and-tracking/, and ONNX/benchmark requests to ../onnx-and-benchmarking/.

Bundled helpers

Use these root-level helper scripts from this generated skill:

  • ../../scripts/check_assets.py — validates required model/config/sample files.
  • ../../scripts/build_native_extensions.py — reproduces the native build steps with explicit commands.
  • ../../scripts/check_core_imports.py — import and constructor smoke test.
  • ../../scripts/bootstrap_runtime.py — shared NumPy/headless/runtime bootstrap used by the wrapper scripts.

Example pattern:

bash
python <skill-root>/scripts/check_assets.py --repo-root <checkout>
python <skill-root>/scripts/build_native_extensions.py --repo-root <checkout>
python <skill-root>/scripts/check_core_imports.py --repo-root <checkout>

Do not copy private environment paths into user-facing notes. Describe the required packages generically: Python 3.10/3.11, torch/torchvision, OpenCV, imageio/imageio-ffmpeg, PyYAML, tqdm, Cython, SciPy, matplotlib, and onnxruntime.

Dependency and backend decisions

  • CPU is sufficient for the selected core workflows.
  • CUDA is an optional speed path through -m gpu; it is not required for the generated skill's baseline verification.
  • Use Cython 0.29.x if FaceBoxes NMS fails under Cython 3.x.
  • Use a NumPy compatibility shim or a conservative NumPy pin if np.long or similar aliases fail at import time.
  • The helper scripts set headless plotting defaults so still-image commands do not block on GUI display in non-interactive environments.

Asset responsibilities

Read references/build-and-assets.md for the build order and asset checklist. Read ../../references/model-assets.md for checkpoint/config purposes and public download notes. Read references/troubleshooting.md for setup-specific failure recovery.

Boundaries

This sub-skill owns setup, assets, and import smoke checks. It does not own choosing demo output modes, video smoothing windows, or interpreting latency numbers except where those tasks fail because setup is incomplete.

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 Setup And Assets AI skill do?

Prepare 3DDFA_V2 runtime assets and native extension builds before demos or benchmarks.

Why use Setup And Assets on TypingMind?

Because you install it once and use it with any model. Setup And Assets 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 Setup And Assets in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/3ddfa-v2/sub-skills/setup-and-assets. 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 Setup And Assets?

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 Setup And Assets?

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

Is the Setup And Assets AI skill free?

Yes. It is published on GitHub by VectorSpaceLab 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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