Add Uint Support
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Use this sub-skill when constructing torchvision datasets, validating dataset roots and downloads, wrapping datasets for transforms v2, decoding or encoding images with torchvision.io, or using…
Install Datasets Io Utils
$ npx skills add VectorSpaceLab/AREX-Skill --skill datasets-io-utils -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill datasets-io-utils --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils .claude/skills/datasets-io-utils && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "datasets-io-utils" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils into .claude/skills/datasets-io-utils/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets-io-utils", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utilsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add VectorSpaceLab/AREX-Skill --skill datasets-io-utils -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill datasets-io-utils --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils .agents/skills/datasets-io-utils && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "datasets-io-utils" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils into .agents/skills/datasets-io-utils/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets-io-utils", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill datasets-io-utils -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill datasets-io-utils --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils .cursor/skills/datasets-io-utils && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "datasets-io-utils" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils into .cursor/skills/datasets-io-utils/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets-io-utils", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add VectorSpaceLab/AREX-Skill --skill datasets-io-utils -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill datasets-io-utils --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils .gemini/skills/datasets-io-utils && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "datasets-io-utils" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils into .gemini/skills/datasets-io-utils/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets-io-utils", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install VectorSpaceLab/AREX-Skill datasets-io-utilsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add VectorSpaceLab/AREX-Skill --skill datasets-io-utils -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils .github/skills/datasets-io-utils && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "datasets-io-utils" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils into .github/skills/datasets-io-utils/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets-io-utils", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill datasets-io-utils -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill datasets-io-utils --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils .opencode/skills/datasets-io-utils && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "datasets-io-utils" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils into .opencode/skills/datasets-io-utils/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datasets-io-utils", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
datasets-io-utils- GitHub stars
- 331
- Token cost
- ~760 tokens
- SKILL.md length
- 282 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 157
- Repo updated
- First seen
- Licence
- BSD-3-Clause
At a glance
Use this sub-skill when constructing torchvision datasets, validating dataset roots and downloads, wrapping datasets for transforms v2, decoding or encoding images with torchvision.io, or using…
- AI & LLM Engineering work in your project
- SKILL.md covers Route by task, Quick safe check, Hard cases this sub-skill… and Boundaries
- Runs Python scripts from its folder; calls python
What it does
Datasets Io Utils is an agent skill from VectorSpaceLab/AREX-Skill. Use this sub-skill when constructing torchvision datasets, validating dataset roots and downloads, wrapping datasets for transforms v2, decoding or encoding images with torchvision.io, or using torchvision.utils visualization helpers.
Its SKILL.md is about 760 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/datasets-and-data-roots.md`, `references/io-and-visualization.md` and `references/troubleshooting.md`).
It sits in AI & LLM Engineering. It works with PyTorch. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is BSD-3-Clause.
When your agent uses it
- AI & LLM Engineering work in your project
Example prompts
- “/datasets-io-utils”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit ac3fe1a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Datasets Io Utils loads about 760 tokens when it runs, and up to ~5.1k if it reads all its reference files. Until then it costs about 63 tokens; SKILL.md has 282 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its BSD-3-Clause licence (© VectorSpaceLab). 282 words, ~760 tokens.
Download SKILL.mdSave it as
.claude/skills/datasets-io-utils/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- datasets-io-utils
- description
- Use this sub-skill when constructing torchvision datasets, validating dataset roots and downloads, wrapping datasets for transforms v2, decoding or encoding images with torchvision.io, or using torchvision.utils visualization helpers.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- BSD 3-Clause
TorchVision Datasets, IO, and Utilities
Use this sub-skill for data ingress and small visual checks in TorchVision workflows: built-in datasets, custom VisionDataset / ImageFolder / DatasetFolder code, safe data roots, wrap_dataset_for_transforms_v2, image decode/encode APIs, and torchvision.utils drawing helpers.
Route by task
- Dataset construction or root layout: use
references/datasets-and-data-roots.mdfor built-in family selection,ImageFolder/DatasetFolderconventions,FakeData, and no-network smoke fixtures. - Transforms v2 dataset wrapping: start here for
wrap_dataset_for_transforms_v2dataset support and target-shape expectations, then route transform pipeline internals to../transforms-and-tv-tensors/. - Image IO and visualization: use
references/io-and-visualization.mdfordecode_image,read_file, encoders/writers,make_grid,draw_bounding_boxes,draw_segmentation_masks,draw_keypoints, andflow_to_image. - Failures and warnings: use
references/troubleshooting.mdfor download races, root layout mistakes, image extension or codec failures, dtype/channel mistakes, video/TorchCodec migration, and visualization label/color errors. - Model inference or weight preprocessing: route to
../models-and-weights/; this sub-skill only covers loading/decoding/visualizing data around those workflows.
Quick safe check
Run the bundled script when an agent needs to verify local TorchVision dataset, image IO, and utility surfaces without network access:
python sub-skills/datasets-io-utils/scripts/check_dataset_io.pyThe script creates a temporary two-class ImageFolder fixture, uses FakeData, attempts torchvision.io PNG round-trip if the image extension is available, and exercises make_grid plus draw_bounding_boxes.
Hard cases this sub-skill supports
- Build a tiny
ImageFolderfixture, validate class discovery/order, attach a transform, and verify tensors without downloading real data. - Diagnose image decode failures by separating path/layout errors, missing image extension support, optional AVIF/HEIC decoder requirements, and PIL fallback options.
Boundaries
- Do not put augmentation policy details here; link to
../transforms-and-tv-tensors/for v2 pipelines, TVTensor metadata, bounding-box transform semantics, and dtype/range conversion details. - Do not cover model output interpretation beyond visualization input requirements; link to
../models-and-weights/or../ops-and-detection/as appropriate. - Treat legacy C++ IO examples as reference-only gaps; prefer public Python
torchvision.ioand TorchCodec migration guidance.
© VectorSpaceLab, BSD-3-Clause. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/datasets-and-data-roots.md
- references/io-and-visualization.md
- references/troubleshooting.md
- scripts/check_dataset_io.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Datasets Io Utils next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Datasets Io Utils this skillVectorSpaceLab/AREX-Skill | 331 | — | ~760 | Automated safety check: Pass | BSD-3-Clause | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Add Torch Shapes Examplefacebook/pyrefly | 7.1k | — | ~1.3k | Automated safety check: Pass | MIT | |
| MUSA GPU Training Optimizeropen-infra-skills/infra-skills | 141 | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Ghstack CIpytorch/pytorch | 104k | — | ~1.4k | Automated safety check: Pass | Custom licence |
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Related
Works with
Categories
Questions about Datasets Io Utils
What does Datasets Io Utils do?
Use this sub-skill when constructing torchvision datasets, validating dataset roots and downloads, wrapping datasets for transforms v2, decoding or encoding images with torchvision.io, or using…. Datasets Io Utils is an agent skill from VectorSpaceLab/AREX-Skill.utils visualization helpers.
When should I use Datasets Io Utils?
Datasets Io Utils fits situations like: AI & LLM Engineering work in your project.
How do I install Datasets Io Utils in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill datasets-io-utils -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils in VectorSpaceLab/AREX-Skill) into .claude/skills/datasets-io-utils in your project. Claude Code loads it when a task matches its description.
How do I install Datasets Io Utils in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill datasets-io-utils -a codex`. Or copy the skill folder (skills/repositories/repo-skills/torchvision/sub-skills/datasets-io-utils in VectorSpaceLab/AREX-Skill) into .agents/skills/datasets-io-utils in your project. Codex loads it when a task matches its description.
Can I use Datasets Io Utils in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add VectorSpaceLab/AREX-Skill --skill datasets-io-utils -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/datasets-io-utils, .gemini/skills/datasets-io-utils, .github/skills/datasets-io-utils and .opencode/skills/datasets-io-utils in your project.
What does Datasets Io Utils need to run?
Going by SKILL.md and its folder, Datasets Io Utils needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Datasets Io Utils access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Datasets Io Utils safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Datasets Io Utils use?
Datasets Io Utils is published under the BSD-3-Clause licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Datasets Io Utils use?
About 760 tokens (SKILL.md is roughly 3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.3k tokens, read only when the agent opens those files.
What are the alternatives to Datasets Io Utils?
Skills that share tags, products or a category with Datasets Io Utils: Add Uint Support (pytorch/pytorch, 104k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), Add Torch Shapes Example (facebook/pyrefly, 7.1k stars) and MUSA GPU Training Optimizer (open-infra-skills/infra-skills, 141 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Datasets Io Utils?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 331 GitHub stars. The repository holds 157 skills in this directory. The repository was last updated on September 3, 2026.
Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.