Setup Build logo

Setup Build

Organization
VectorSpaceLab
setup-build

Guides legacy-compatible AdelaiDet installation, Detectron2/PyTorch/CUDA version selection, editable extension builds, and runtime smoke checks.

Overview

PublisherVectorSpaceLab
RepositoryAREX-Skill
Skill namesetup-build
Stars
341
Forks
26
Bundled files
2
LicenseNOASSERTION
Links
  • 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 Build 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/adelai-det/sub-skills/setup-build
  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/adelai-det/sub-skills/setup-build .claude/skills/setup-build
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Setup Build 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 Build 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 Build 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-build

Use this sub-skill when a task involves installing AdelaiDet, selecting PyTorch/Detectron2/CUDA versions, building adet._C, checking custom operators, or diagnosing import/build failures.

Use this route for

  • Creating a fresh environment for AdelaiDet.
  • Choosing a Detectron2 wheel that matches PyTorch and CUDA.
  • Building AdelaiDet editable from source.
  • Verifying adet.config.get_cfg, Detectron2 registries, adet._C, BezierAlign, DefROIAlign, and multi-label NMS.
  • Fixing build failures such as missing torch in build isolation, missing CUDA headers, missing THC headers, Pillow/rapidfuzz/OpenCV incompatibilities, or CUDA architecture problems.

Do not use this route for

  • Choosing a model config after installation succeeds. Use ../train-eval/SKILL.md.
  • Demo commands with an already working install. Use ../demo-visualize/SKILL.md.
  • Dataset annotation conversion. Use ../data-prep/SKILL.md.
  • ONNX and checkpoint conversion. Use ../export-convert/SKILL.md.

Read first

  • references/setup-build.md for the known-good install recipe and version pins.
  • references/runtime-checks.md for what each smoke check proves.
  • ../../references/compatibility.md for package-version rationale.
  • ../../references/troubleshooting.md for cross-cutting error fixes.

Skill-owned scripts

  • ../../scripts/check_install.py — package/config/registry/extension smoke check, with optional --cuda-ops.

Standard workflow

  1. Create the compatible Python/PyTorch/CUDA/Detectron2 environment described in references/setup-build.md.

  2. Install AdelaiDet with python -m pip install --no-build-isolation -e <checkout>.

  3. Pin compatibility fixes: Pillow<10, rapidfuzz<3, NumPy 1.23.x, and OpenCV headless 4.8.x.

  4. Run:

    bash
    python ../../scripts/check_install.py --cuda-ops
  5. If --cuda-ops fails, do not start training or inference; fix setup first or explicitly narrow the task to import/config-only analysis.

Decision points

  • Need unmodified CUDA extension? Use PyTorch 1.10.x + CUDA 11.3. PyTorch 2.x is not the default.
  • No GPU available? You may perform import/config-only checks without --cuda-ops, but record that DefROIAlign and native _C.ml_nms are not validated.
  • Need ONNX/TensorRT/NCNN? First prove the base AdelaiDet install, then switch to export-convert for optional deployment runtimes.

Success signal

A healthy CUDA-capable environment reports:

  • import adet succeeds.
  • adet._C exposes BezierAlign, DefROIAlign, and ml_nms symbols.
  • scripts/check_install.py --cuda-ops exits 0.
  • CLI help for training/demo/visualization/export can import all required modules.

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 Build AI skill do?

Guides legacy-compatible AdelaiDet installation, Detectron2/PyTorch/CUDA version selection, editable extension builds, and runtime smoke checks.

Why use Setup Build on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/adelai-det/sub-skills/setup-build. 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 Build?

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 Build?

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

Is the Setup Build AI skill free?

Yes. It is published on GitHub by VectorSpaceLab under the NOASSERTION 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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