Benchmark Pyrefly
facebook/pyrefly
Run Pyrefly benchmarks locally via Buck or Cargo, including PyTorch real-world LSP benchmarks.
A skill your agent uses when working with TorchVision models, weights, transforms, TVTensors, datasets, image IO, visualization utilities, vision ops, detection helpers, or official reference…
Install Torchvision
$ npx skills add VectorSpaceLab/AREX-Skill --skill torchvision -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill torchvision --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 .claude/skills/torchvision && 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 "torchvision" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision into .claude/skills/torchvision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torchvision", 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/torchvisionType 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 torchvision -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill torchvision --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 .agents/skills/torchvision && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "torchvision" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision into .agents/skills/torchvision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torchvision", 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 torchvision -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill torchvision --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 .cursor/skills/torchvision && 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 "torchvision" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision into .cursor/skills/torchvision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torchvision", 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--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 torchvision -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill torchvision --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 .gemini/skills/torchvision && 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 "torchvision" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision into .gemini/skills/torchvision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torchvision", 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 torchvisionInstalls 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 torchvision -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 .github/skills/torchvision && 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 "torchvision" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision into .github/skills/torchvision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torchvision", 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 torchvision -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 torchvision --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 .opencode/skills/torchvision && 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 "torchvision" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/torchvision into .opencode/skills/torchvision/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "torchvision", 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
torchvision- GitHub stars
- 330
- Token cost
- ~1.1k tokens
- SKILL.md length
- 406 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 159
- Repo updated
- First seen
- Licence
- BSD-3-Clause
At a glance
A skill your agent uses when working with TorchVision models, weights, transforms, TVTensors, datasets, image IO, visualization utilities, vision ops, detection helpers, or official reference…
- Works in 4 steps: Confirm installation and compatibility… → Use references/package-overview.md for… → Use references/troubleshooting.md when… → …
- Working with TorchVision models
- SKILL.md covers Start Here, Route by Task, Minimal Import Check and Common Safe Defaults, plus 2 more sections
- Runs Python scripts from its folder; calls python
What it does
Torchvision is an agent skill from VectorSpaceLab/AREX-Skill. Use this skill when working with TorchVision models, weights, transforms, TVTensors, datasets, image IO, visualization utilities, vision ops, detection helpers, or official reference training workflows.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/package-overview.md`, `references/repo-provenance.md` and `references/repo-routing-metadata.json`).
It sits in AI & LLM Engineering. It works with PyTorch and Python. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is BSD-3-Clause.
When your agent uses it
- Working with TorchVision models
- Visualization utilities
- Detection helpers
- Official reference training workflows
Example prompts
- “/torchvision”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Confirm installation and compatibility with python scripts/check_torchvision_install.py.
- Use references/package-overview.md for the module map, supported surfaces, and install/runtime assumptions.
- Use references/troubleshooting.md when imports, compiled ops, downloads, codecs, datasets, or version matching fail.
- Route to the focused sub-skill below instead of treating this root file as a manual.
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
Torchvision loads about 1.1k tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 54 tokens; SKILL.md has 406 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). 406 words, ~1,095 tokens.
Download SKILL.mdSave it as
.claude/skills/torchvision/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- torchvision
- description
- Use this skill when working with TorchVision models, weights, transforms, TVTensors, datasets, image IO, visualization utilities, vision ops, detection helpers, or official reference training workflows.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- BSD 3-Clause
TorchVision Repo Skill
Use this skill for practical TorchVision work: choosing models and pretrained weights, building transform pipelines, preparing datasets and image IO checks, using detection/box operators, and planning official reference training commands safely.
TorchVision is a PyTorch computer-vision library. It provides public Python APIs for model architectures, pretrained weight metadata, datasets, transforms, TVTensor metadata types, image IO, visualization helpers, and vision-specific operators.
Start Here
- Confirm installation and compatibility with
python scripts/check_torchvision_install.py. - Use
references/package-overview.mdfor the module map, supported surfaces, and install/runtime assumptions. - Use
references/troubleshooting.mdwhen imports, compiled ops, downloads, codecs, datasets, or version matching fail. - Route to the focused sub-skill below instead of treating this root file as a manual.
Route by Task
- Model selection, pretrained weights, weight transforms, output interpretation, PyTorch Hub, or feature extraction: use
sub-skills/models-and-weights/. - Transform pipelines, v2 migration, TVTensor metadata, boxes/masks/keypoints, random transform behavior, or transform performance: use
sub-skills/transforms-and-tv-tensors/. - Built-in/custom datasets, data roots,
ImageFolder,FakeData, image decode/encode, visualization utilities, or no-network fixtures: usesub-skills/datasets-io-utils/. - Box utilities, NMS, ROI Align/Pool, FPN helpers, detection postprocessing, losses/layers, or custom operator errors: use
sub-skills/ops-and-detection/. - Official reference training/evaluation scripts, distributed command planning, dataset layout requirements, and safe dry-run training plans: use
sub-skills/training-references/.
Minimal Import Check
python - <<'PY'
import torch
import torchvision
print('torch', torch.__version__)
print('torchvision', torchvision.__version__)
print('ops loaded', torchvision.extension._has_ops())
PYIf ops loaded is false, pure-Python surfaces may still import, but detection ops such as torchvision.ops.nms and many detection models can fail. Use references/troubleshooting.md and sub-skills/ops-and-detection/references/troubleshooting.md.
Show full SKILL.md (181 more words)Show less
Common Safe Defaults
- Use
weights=Nonewhen tests or examples must avoid network downloads. - Use weight enums and
weights.transforms()for real pretrained inference; do not recreate preprocessing by hand unless the task requires it. - Prefer
torchvision.transforms.v2for new transform pipelines, especially when samples include boxes, masks, videos, or keypoints. - Use tiny fixtures,
FakeData, and bundled smoke scripts before touching real dataset roots or network downloads. - Treat reference training scripts as command plans by default; they can require datasets, GPUs, distributed launch, and latest-source compatibility.
Bundled Checks
scripts/check_torchvision_install.py: verifies import, versions, extension availability, important submodules, and no-download smoke surfaces.sub-skills/models-and-weights/scripts/inspect_models.py: lists models/weights and inspects safe model metadata.sub-skills/transforms-and-tv-tensors/scripts/smoke_transform_pipeline.py: checks v2 transforms and TVTensor metadata on tiny tensors.sub-skills/datasets-io-utils/scripts/check_dataset_io.py: creates a tiny no-network dataset/IO fixture.sub-skills/ops-and-detection/scripts/smoke_ops.py: checks small CPU box/NMS/ROI operator behavior.sub-skills/training-references/scripts/inspect_reference_args.py: summarizes safe reference-training command families without importing source scripts.
Evidence and Staleness
Read references/repo-provenance.md before trusting this skill for a modified checkout or a new TorchVision release. Refresh the skill if the source commit, public APIs, docs, model catalog, transform semantics, dataset list, compiled ops behavior, or reference scripts changed substantially.
© 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 6 other files (scripts, references) in skills/repositories/repo-skills/torchvision of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/package-overview.md
- references/repo-provenance.md
- references/repo-routing-metadata.json
- references/troubleshooting.md
- scripts/check_torchvision_install.py
- sub-skills
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Torchvision 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 |
|---|---|---|---|---|---|---|
| Torchvision this skillVectorSpaceLab/AREX-Skill | 330 | — | ~1.1k | Automated safety check: Pass | BSD-3-Clause | |
| Benchmark Pyreflyfacebook/pyrefly | 7.1k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Document Public APIspytorch/pytorch | 104k | — | ~4.2k | Automated safety check: Pass | Custom licence | |
| ExecuTorch Cortex-M Backendpytorch/executorch | 5.1k | — | ~872 | Automated safety check: Pass | Custom licence | |
| Homepage Generatorwanshuiyin/ARIS-in-AI-Offer | 583 | — | ~4.8k | Automated safety check: Notes | MIT | |
| Ako4allTongmingLAIC/AKO4ALL | 369 | — | ~4k | Automated safety check: Pass | MIT |
Similar skills
- Official
Document Public APIs
pytorch/pytorch
Document undocumented public APIs in PyTorch by removing functions from coverageignorefunctions and coverageignoreclasses in docs/source/conf.py, running Sphinx coverage, and adding the appropriate…
ExecuTorch Cortex-M Backend
pytorch/executorch
Developer guide for the Cortex-M (CMSIS-NN) backend in ExecuTorch: quantization pipeline, pass manager, tests and adding new ops.
Homepage Generator
wanshuiyin/ARIS-in-AI-Offer
Generate a fact-checked academic personal homepage from a CV, optionally augmented by an existing manual homepage and an assets directory.
Ako4all
TongmingLAIC/AKO4ALL
Drive an agentic loop that iteratively optimizes a GPU kernel for maximum speedup.
PyTorch Lightning Training
Orchestra-Research/AI-Research-SKILLs
Shows how to organize PyTorch training with Lightning's LightningModule and Trainer, covering validation, DDP, callbacks and learning-rate scheduling.
More from VectorSpaceLab/AREX-Skill
All 159 skills in this repoAgent Lightning
VectorSpaceLab/AREX-Skill
Use this repo skill for Agent Lightning package tasks: authoring trainable agents, tracing rewards and spans, running LightningStore/Trainer loops, using agl CLI services, choosing examples, and…
Agent Tools
VectorSpaceLab/AREX-Skill
A skill your agent uses when configuring LiteLLM for MCP tools, A2A agents, Claude Code/Cursor agent gateway traffic, MCP auth/OAuth, tool permissions, semantic filtering, or agent-specific proxy…
Agents And Awel
VectorSpaceLab/AREX-Skill
Build and debug DB-GPT agents, tools, skills, teams, and AWEL workflows, including deterministic local DAG runs and HTTP-trigger topology without assuming an LLM, credential, or external service.
Agents And Middleware
VectorSpaceLab/AREX-Skill
Work on the actively maintained LangChain v1 agent package: initchatmodel, createagent, structured output, tools, middleware, embeddings initialization, provider routing, and agent runtime…
Agents Workflows
VectorSpaceLab/AREX-Skill
A skill your agent uses for giskard.agents async chat workflows, tools, prompt templates, structured outputs, retries, rate limiting, embeddings, and optional LiteLLM backend.
Alphafold3
VectorSpaceLab/AREX-Skill
A skill your agent uses for AlphaFold 3 input preparation, prediction command planning, output interpretation, and Python API inspection.
Related
Categories
Questions about Torchvision
What does Torchvision do?
A skill your agent uses when working with TorchVision models, weights, transforms, TVTensors, datasets, image IO, visualization utilities, vision ops, detection helpers, or official reference…. Torchvision is an agent skill from VectorSpaceLab/AREX-Skill. Use this skill when working with TorchVision models, weights, transforms, TVTensors, datasets, image IO, visualization utilities, vision ops, detection helpers, or official reference training workflows.
When should I use Torchvision?
Torchvision fits situations like: working with TorchVision models; visualization utilities; detection helpers; official reference training workflows.
How do I install Torchvision in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill torchvision -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/torchvision in VectorSpaceLab/AREX-Skill) into .claude/skills/torchvision in your project. Claude Code loads it when a task matches its description.
How do I install Torchvision in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill torchvision -a codex`. Or copy the skill folder (skills/repositories/repo-skills/torchvision in VectorSpaceLab/AREX-Skill) into .agents/skills/torchvision in your project. Codex loads it when a task matches its description.
Can I use Torchvision 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 torchvision -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/torchvision, .gemini/skills/torchvision, .github/skills/torchvision and .opencode/skills/torchvision in your project.
What does Torchvision need to run?
Going by SKILL.md and its folder, Torchvision needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Torchvision 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 Torchvision 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 Torchvision use?
Torchvision 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 Torchvision use?
About 1.1k tokens (SKILL.md is roughly 4.4k 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 1.9k tokens, read only when the agent opens those files.
What are the alternatives to Torchvision?
Skills that share tags, products or a category with Torchvision: Benchmark Pyrefly (facebook/pyrefly, 7.1k stars), Document Public APIs (pytorch/pytorch, 104k stars), ExecuTorch Cortex-M Backend (pytorch/executorch, 5.1k stars) and Homepage Generator (wanshuiyin/ARIS-in-AI-Offer, 583 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Torchvision?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 330 GitHub stars. The repository holds 159 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.