Add Uint Support
pytorch/pytorch
Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.
Use Towhee's optional PyTorch Trainer, TrainingConfig YAML, NNOperator training bridge, and towhee.models package boundaries safely.
Install Training And Models
$ npx skills add VectorSpaceLab/AREX-Skill --skill training-and-models -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill training-and-models --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/towhee/sub-skills/training-and-models .claude/skills/training-and-models && 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 "training-and-models" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/towhee/sub-skills/training-and-models into .claude/skills/training-and-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-and-models", 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/towhee/sub-skills/training-and-modelsType 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 training-and-models -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill training-and-models --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/towhee/sub-skills/training-and-models .agents/skills/training-and-models && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "training-and-models" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/towhee/sub-skills/training-and-models into .agents/skills/training-and-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-and-models", 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 training-and-models -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill training-and-models --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/towhee/sub-skills/training-and-models .cursor/skills/training-and-models && 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 "training-and-models" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/towhee/sub-skills/training-and-models into .cursor/skills/training-and-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-and-models", 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/towhee/sub-skills/training-and-models--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 training-and-models -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill training-and-models --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/towhee/sub-skills/training-and-models .gemini/skills/training-and-models && 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 "training-and-models" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/towhee/sub-skills/training-and-models into .gemini/skills/training-and-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-and-models", 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 training-and-modelsInstalls 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 training-and-models -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/towhee/sub-skills/training-and-models .github/skills/training-and-models && 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 "training-and-models" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/towhee/sub-skills/training-and-models into .github/skills/training-and-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-and-models", 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 training-and-models -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 training-and-models --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/towhee/sub-skills/training-and-models .opencode/skills/training-and-models && 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 "training-and-models" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/towhee/sub-skills/training-and-models into .opencode/skills/training-and-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-and-models", 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
training-and-models- GitHub stars
- 331
- Token cost
- ~676 tokens
- SKILL.md length
- 244 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 157
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Use Towhee's optional PyTorch Trainer, TrainingConfig YAML, NNOperator training bridge, and towhee.models package boundaries safely.
- Tasks that involve Deep learning
- SKILL.md covers Fast routing, Boundaries and handoff and Default safe workflow
- Runs Python scripts from its folder; calls python
What it does
Training And Models is an agent skill from VectorSpaceLab/AREX-Skill. Use Towhee's optional PyTorch Trainer, TrainingConfig YAML, NNOperator training bridge, and towhee.models package boundaries safely.
Its SKILL.md is about 680 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/model-zoo-boundaries.md`, `references/training-config.md` and `references/troubleshooting.md`).
It sits in AI & LLM Engineering, covering Deep learning. It works with PyTorch. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Deep learning
Example prompts
- “/training-and-models”
Requirements
- Python 3
- Docker
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
Training And Models loads about 676 tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 38 tokens; SKILL.md has 244 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 Apache-2.0 licence (© VectorSpaceLab). 244 words, ~676 tokens.
Download SKILL.mdSave it as
.claude/skills/training-and-models/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- training-and-models
- description
- Use Towhee's optional PyTorch Trainer, TrainingConfig YAML, NNOperator training bridge, and towhee.models package boundaries safely.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- Apache 2.0
Towhee training and models
Use this sub-skill when the task involves Towhee's optional PyTorch training layer: TrainingConfig, Trainer, NNOperator.train(...), NNOperator.setup_trainer(...), checkpoint/model-card behavior, or deciding whether to install towhee.models instead of using Hub operators.
Fast routing
- For
TrainingConfigfields, YAML save/load shape, device choices, loss/optimizer/scheduler names,Trainer, andNNOperatortraining, use Training configuration and Trainer. - For the
towhee.modelspackage split,create_model(...)pattern, pretrained-download risk, and Hub-versus-model-zoo decisions, use Model-zoo boundaries. - For optional PyTorch/TorchVision/TorchMetrics installs, Towhee auto-install side effects, missing trainable model attributes, CUDA/CPU device mistakes, and model-download failures, use Troubleshooting.
- To produce a CPU-safe config stub without importing Towhee or torch, run training_config_template.py. Add
--check-importsonly in an isolated environment where importing the optional trainer stack is acceptable.
Boundaries and handoff
- Stay here for high-level training configuration,
Trainersetup,NNOperatortraining hooks, checkpoint/model-card expectations, and model-zoo installation boundaries. - Route custom pipeline operators,
ops,register, Hub revisions, andtowhee initto operator-hub-and-cli. - Route
DataCollection,DataLoader, image/audio/video wrappers, and non-training data serialization todata-utilities. - Route HTTP/GRPC services,
towhee server, Docker, Triton, and deployment packaging toserving-and-triton. - Do not use this sub-skill as an exhaustive per-model API catalog. Treat large model tests, pretrained downloads, and full fine-tuning runs as explicit user-approved optional work.
Default safe workflow
python scripts/training_config_template.py --format yaml --output training_config.yamlStart with device_str: cpu, dataloader_num_workers: 0, tensorboard: null, and pretrained=False model choices when the user only needs a portable template or diagnosis. Move to actual training only after the active environment has the intended PyTorch stack and any model/data downloads are approved.
© VectorSpaceLab, Apache-2.0. 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/towhee/sub-skills/training-and-models of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/model-zoo-boundaries.md
- references/training-config.md
- references/troubleshooting.md
- scripts/training_config_template.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Training And Models 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 |
|---|---|---|---|---|---|---|
| Training And Models this skillVectorSpaceLab/AREX-Skill | 331 | — | ~676 | Automated safety check: Pass | Apache-2.0 | |
| 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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Add Torch Shapes Example
facebook/pyrefly
A skill your agent uses when adding a new PyTorch model to Pyrefly's shape-tracking example corpus under tensor-shapes/pyrefly-torch-stubs/examples — i.e.
MUSA GPU Training Optimizer
open-infra-skills/infra-skills
Profiles, benchmarks and tunes AI training workloads on Moore Threads MUSA GPUs with a measurement-first process that keeps model behavior unchanged.
Ghstack CI
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Manage CI for PyTorch ghstack stacks by running CI where its results are useful now and deferring other PRs with [no-ci].
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Related
Works with
Categories
Questions about Training And Models
What does Training And Models do?
Use Towhee's optional PyTorch Trainer, TrainingConfig YAML, NNOperator training bridge, and towhee.models package boundaries safely. Training And Models is an agent skill from VectorSpaceLab/AREX-Skill.models package boundaries safely.
When should I use Training And Models?
Training And Models fits situations like: tasks that involve Deep learning.
How do I install Training And Models in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill training-and-models -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/towhee/sub-skills/training-and-models in VectorSpaceLab/AREX-Skill) into .claude/skills/training-and-models in your project. Claude Code loads it when a task matches its description.
How do I install Training And Models in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill training-and-models -a codex`. Or copy the skill folder (skills/repositories/repo-skills/towhee/sub-skills/training-and-models in VectorSpaceLab/AREX-Skill) into .agents/skills/training-and-models in your project. Codex loads it when a task matches its description.
Can I use Training And Models 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 training-and-models -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/training-and-models, .gemini/skills/training-and-models, .github/skills/training-and-models and .opencode/skills/training-and-models in your project.
What does Training And Models need to run?
Going by SKILL.md and its folder, Training And Models needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3; Docker.
Does Training And Models 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 Training And Models 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 Training And Models use?
Training And Models is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Training And Models use?
About 676 tokens (SKILL.md is roughly 2.7k 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 Training And Models?
Skills that share tags, products or a category with Training And Models: 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 Training And Models?
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.