AI Research Reproduction
lllllllama/RigorPilot-Skills
Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction.
Compose timm API-level training loops with optimizer and scheduler factories, loss selection, task wrappers, EMA, AMP scaling, metrics, checkpoint state, and safe CPU smoke checks.
Install Training Workflows
$ npx skills add VectorSpaceLab/AREX-Skill --skill training-workflows -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill training-workflows --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/timm/sub-skills/training-workflows .claude/skills/training-workflows && 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-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/timm/sub-skills/training-workflows into .claude/skills/training-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-workflows", 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/timm/sub-skills/training-workflowsType 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-workflows -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill training-workflows --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/timm/sub-skills/training-workflows .agents/skills/training-workflows && 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-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/timm/sub-skills/training-workflows into .agents/skills/training-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-workflows", 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-workflows -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill training-workflows --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/timm/sub-skills/training-workflows .cursor/skills/training-workflows && 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-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/timm/sub-skills/training-workflows into .cursor/skills/training-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-workflows", 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/timm/sub-skills/training-workflows--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-workflows -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill training-workflows --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/timm/sub-skills/training-workflows .gemini/skills/training-workflows && 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-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/timm/sub-skills/training-workflows into .gemini/skills/training-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-workflows", 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-workflowsInstalls 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-workflows -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/timm/sub-skills/training-workflows .github/skills/training-workflows && 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-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/timm/sub-skills/training-workflows into .github/skills/training-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-workflows", 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-workflows -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-workflows --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/timm/sub-skills/training-workflows .opencode/skills/training-workflows && 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-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/timm/sub-skills/training-workflows into .opencode/skills/training-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "training-workflows", 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-workflows- GitHub stars
- 328
- Token cost
- ~733 tokens
- SKILL.md length
- 248 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 157
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Compose timm API-level training loops with optimizer and scheduler factories, loss selection, task wrappers, EMA, AMP scaling, metrics, checkpoint state, and safe CPU smoke checks.
- Debugging custom training code rather than cataloging train.py CLI flags
- SKILL.md covers Route by Need, Boundaries and Safe Defaults
- Runs Python scripts from its folder; calls python
- Building data loaders
What it does
Training Workflows is an agent skill from VectorSpaceLab/AREX-Skill. Compose timm API-level training loops with optimizer and scheduler factories, loss selection, task wrappers, EMA, AMP scaling, metrics, checkpoint state, and safe CPU smoke checks. Use when writing or debugging custom training code rather than cataloging train.py CLI flags or building data loaders.
Its SKILL.md is about 730 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/optimizer-scheduler-guide.md`, `references/training-api.md` and `references/troubleshooting.md`).
It sits in AI & LLM Engineering, covering Deep learning. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is Apache-2.0.
When your agent uses it
- Debugging custom training code rather than cataloging train.py CLI flags
- Building data loaders
Example prompts
- “/training-workflows”
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
Training Workflows loads about 733 tokens when it runs, and up to ~6k if it reads all its reference files. Until then it costs about 80 tokens; SKILL.md has 248 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). 248 words, ~733 tokens.
Download SKILL.mdSave it as
.claude/skills/training-workflows/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- training-workflows
- description
- Compose timm API-level training loops with optimizer and scheduler factories, loss selection, task wrappers, EMA, AMP scaling, metrics, checkpoint state, and safe CPU smoke checks. Use when writing or debugging custom training code rather than cataloging train.py CLI flags or building data loaders.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- Apache 2.0
Training Workflows
Use this sub-skill when an agent needs to assemble or debug timm training components in Python code: create_optimizer_v2, optimizer_kwargs, create_scheduler_v2, loss modules, task wrappers, EMA, AMP scaling, metrics, and checkpoint-saving concepts.
Route by Need
- Optimizer or parameter groups: Start with
references/optimizer-scheduler-guide.mdforcreate_optimizer_v2, weight-decay filtering, layer decay, hybrid fallback groups, andoptimizer_kwargsconfig translation. - LR schedule behavior: Use
references/optimizer-scheduler-guide.mdforcreate_scheduler_v2, epoch-vs-update stepping, warmup, cycles, plateau metrics, and returned adjusted epoch counts. - Losses and task wrappers: Use
references/training-api.mdforLabelSmoothingCrossEntropy,SoftTargetCrossEntropy,BinaryCrossEntropy,JsdCrossEntropy,ClassificationTask, and distillation task routing. - EMA, AMP, metrics, checkpoints: Use
references/training-api.mdforModelEmavariants,NativeScaler,accuracy,AverageMeter, task checkpoint state, andCheckpointSaverconcepts. - Failure diagnosis: Use
references/troubleshooting.mdfor unsupported optimizer names, layer-decay grouping, scheduler step confusion, mixup/BCE target shape, distillation shape mismatches, EMA resume, and AMP/device mismatch. - Smoke construction: Run or adapt
scripts/training_api_smoke.pyto verify a model, optimizer, scheduler, loss, optional task wrapper, metrics, EMA, and one CPU backward pass.
Boundaries
- This sub-skill covers API-level composition. Leave train script flag catalogs and launch recipes to
cli-workflows. - This sub-skill assumes tensors already come from a valid pipeline. Leave dataset, loader, transforms, mixup/cutmix construction, and collation details to
data-pipelines. - Do not make generated examples depend on repository-local files; keep examples importable from installed
timmand standard PyTorch.
Safe Defaults
For a minimal custom loop, prefer create_model(..., pretrained=False), create_optimizer_v2(model, opt='adamw', lr=...), create_scheduler_v2(optimizer, sched='cosine', num_epochs=..., warmup_epochs=...), a target-compatible loss, accuracy/AverageMeter for logging, and optional ModelEmaV3 after model/device placement.
Run the smoke script before recommending a larger recipe:
python scripts/training_api_smoke.py --model resnet18 --opt adamw --sched cosine© 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/timm/sub-skills/training-workflows of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/optimizer-scheduler-guide.md
- references/training-api.md
- references/troubleshooting.md
- scripts/training_api_smoke.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Training Workflows 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 Workflows this skillVectorSpaceLab/AREX-Skill | 328 | — | ~733 | Automated safety check: Pass | Apache-2.0 | |
| AI Research Reproductionlllllllama/RigorPilot-Skills | 497 | 1 repos | ~1.8k | Automated safety check: Pass | MIT | |
| Onnxtxtonnx/onnx | 22k | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Performance Optimizationalbumentations-team/AlbumentationsX | 567 | — | ~1.7k | Automated safety check: Pass | AGPL-3.0 | |
| Extending Ocannlahrefs/ocannl | 118 | — | ~728 | Automated safety check: Pass | BSD-2-Clause | |
| Explore Codelllllllama/RigorPilot-Skills | 497 | 1 repos | ~648 | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Training Workflows
What does Training Workflows do?
Compose timm API-level training loops with optimizer and scheduler factories, loss selection, task wrappers, EMA, AMP scaling, metrics, checkpoint state, and safe CPU smoke checks. Training Workflows is an agent skill from VectorSpaceLab/AREX-Skill. Compose timm API-level training loops with optimizer and scheduler factories, loss selection, task wrappers, EMA, AMP scaling, metrics, checkpoint state, and safe CPU smoke checks.
When should I use Training Workflows?
Training Workflows fits situations like: debugging custom training code rather than cataloging train.py CLI flags; building data loaders.
How do I install Training Workflows in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill training-workflows -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/timm/sub-skills/training-workflows in VectorSpaceLab/AREX-Skill) into .claude/skills/training-workflows in your project. Claude Code loads it when a task matches its description.
How do I install Training Workflows in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill training-workflows -a codex`. Or copy the skill folder (skills/repositories/repo-skills/timm/sub-skills/training-workflows in VectorSpaceLab/AREX-Skill) into .agents/skills/training-workflows in your project. Codex loads it when a task matches its description.
Can I use Training Workflows 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-workflows -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-workflows, .gemini/skills/training-workflows, .github/skills/training-workflows and .opencode/skills/training-workflows in your project.
What does Training Workflows need to run?
Going by SKILL.md and its folder, Training Workflows needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Training Workflows 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 Workflows 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 Workflows use?
Training Workflows 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 Workflows use?
About 733 tokens (SKILL.md is roughly 2.9k 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 5.3k tokens, read only when the agent opens those files.
What are the alternatives to Training Workflows?
Skills that share tags, products or a category with Training Workflows: AI Research Reproduction (lllllllama/RigorPilot-Skills, 497 stars), Onnxtxt (onnx/onnx, 22k stars), Performance Optimization (albumentations-team/AlbumentationsX, 567 stars) and Extending Ocannl (ahrefs/ocannl, 118 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Training Workflows?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 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.