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Agent skill

Training Data

by VectorSpaceLab in VectorSpaceLab/AREX-Skill

A skill your agent uses when preparing RobustVideoMatting datasets, DATAPATHS, augmentations, losses, or four-stage training commands with GPU and data-layout caveats.

GPL-3.0Auto-check passed

Install Training Data

skills CLI
$ npx skills add VectorSpaceLab/AREX-Skill --skill training-data -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install VectorSpaceLab/AREX-Skill training-data --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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/robust-video-matting/sub-skills/training-data .claude/skills/training-data && rm -rf skills-src

Use ~/.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/

Facts

Skill name
training-data
GitHub stars
330
Token cost
~1k tokens
SKILL.md length
387 words
Files
6 (incl. scripts, references)
Skills in repo
159
Repo updated
First seen
Licence
GPL-3.0

At a glance

A skill your agent uses when preparing RobustVideoMatting datasets, DATAPATHS, augmentations, losses, or four-stage training commands with GPU and data-layout caveats.

  • Works in 5 steps: Treat full RVM training as a large… → Prepare dataset roots using the schemas in → Validate obvious directory mistakes… → …
  • Preparing RobustVideoMatting datasets
  • SKILL.md covers Read this when, Training setup workflow, Bundled references and script and Key decisions, plus 1 more section
  • Runs Python scripts from its folder; calls python

What it does

Training Data is an agent skill from VectorSpaceLab/AREX-Skill. Use when preparing RobustVideoMatting datasets, DATAPATHS, augmentations, losses, or four-stage training commands with GPU and data-layout caveats.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/data-layouts.md`, `references/dataset-api.md` and `references/training-reference.md`).

The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is GPL-3.0.

When your agent uses it

  • Preparing RobustVideoMatting datasets
  • Four-stage training commands with GPU and data-layout caveats

Example prompts

  • “/training-data”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Treat full RVM training as a large multi-GPU workflow, not a smoke test. The
  2. Prepare dataset roots using the schemas in
  3. Validate obvious directory mistakes before launching training
  4. Choose the stage command from
  5. Validate failures against

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 Data loads about 1k tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 41 tokens; SKILL.md has 387 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5k

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 GPL-3.0 licence (© VectorSpaceLab). 387 words, ~1,005 tokens.

Download SKILL.mdSave it as .claude/skills/training-data/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
training-data
description
Use when preparing RobustVideoMatting datasets, DATA_PATHS, augmentations, losses, or four-stage training commands with GPU and data-layout caveats.
disable-model-invocation
true
metadata.disco-role
operating
license
GPL 3.0

RobustVideoMatting Training and Data

Use this sub-skill for training setup, dataset schemas, path validation, augmentation behavior, and adapting the official four-stage RVM training recipe.

Read this when

  • The user asks how to train or fine-tune RVM.
  • The task mentions train.py, train_config.py, DATA_PATHS, VideoMatte240K, ImageMatte, COCO panoptic, Supervisely Person Dataset, or YouTubeVIS.
  • You need to validate dataset folders before editing a config.
  • The user hits training dependency, dataloader, NCCL, CUDA, OOM, or path errors.

Route other tasks elsewhere:

Training setup workflow

  1. Treat full RVM training as a large multi-GPU workflow, not a smoke test. The official training reference used data-center scale hardware and large external datasets.

  2. Prepare dataset roots using the schemas in references/data-layouts.md. Edit the DATA_PATHS mapping in a local training checkout to point at those roots.

  3. Validate obvious directory mistakes before launching training:

    bash
    python scripts/rvm_validate_data_layout.py \
  --videomatte-train /data/VideoMatte240K_JPEG_SD/train \
  --background-images-train /data/Backgrounds/train \
  --background-videos-train /data/BackgroundVideos/train \
  --strict

  4. Choose the stage command from references/training-reference.md, then adjust paths, GPU count, --num-workers, batch size, checkpoint locations, and logging directories for the target machine.

  5. Validate failures against references/troubleshooting.md before retrying a long run.

Bundled references and script

Show full SKILL.md (134 more words)Show less

Key decisions

  • Use mobilenetv3 for the documented official stage examples unless the user asks to train the ResNet50 variant.
  • Do not start train.py on CPU as a validation shortcut. The script discovers torch.cuda.device_count() and uses multiprocessing, NCCL, DDP, SyncBatchNorm, and AMP-oriented logic.
  • Reduce --num-workers first when dataloaders exit unexpectedly on machines with limited CPU memory.
  • Keep exact legacy requirement pins as historical repo evidence. For modern inspection or helper execution, use compatible PyTorch/TorchVision versions, but do not claim that full legacy training was verified unless it was.

Acceptance check for training/data answers

A good answer names the dataset roots and expected subdirectories, distinguishes matting and segmentation datasets, gives the stage command or config change, states GPU/data scale limitations, recommends a safe layout validation step, and avoids presenting full training as a quick smoke test.

© VectorSpaceLab, GPL-3.0. 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 5 other files (scripts, references) in skills/repositories/repo-skills/robust-video-matting/sub-skills/training-data of VectorSpaceLab/AREX-Skill.

  • SKILL.md
  • references/data-layouts.md
  • references/dataset-api.md
  • references/training-reference.md
  • references/troubleshooting.md
  • scripts/rvm_validate_data_layout.py

Open the folder on GitHubat commit ac3fe1a

Compare with similar skills

Training Data 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.

Training Data compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Training Data this skillVectorSpaceLab/AREX-Skill330—~1kAutomated safety check: PassGPL-3.0
Dataset Curationwshobson/agents40k—~2kAutomated safety check: PassMIT
Ito Trainingaffaan-m/ECC276k1 repos~1.5kAutomated safety check: PassMIT
Ray Train Distributed TrainingOrchestra-Research/AI-Research-SKILLs13k2 repos~2.7kAutomated safety check: PassMIT
DatasetsArize-ai/phoenix12k—~1.6kAutomated safety check: PassCustom licence
Audit Preparationsickn33/agentic-awesome-skills47k1 repos~5.3kAutomated safety check: PassMIT

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Questions about Training Data

What does Training Data do?

A skill your agent uses when preparing RobustVideoMatting datasets, DATAPATHS, augmentations, losses, or four-stage training commands with GPU and data-layout caveats. Training Data is an agent skill from VectorSpaceLab/AREX-Skill. Use when preparing RobustVideoMatting datasets, DATAPATHS, augmentations, losses, or four-stage training commands with GPU and data-layout caveats.

When should I use Training Data?

Training Data fits situations like: preparing RobustVideoMatting datasets; four-stage training commands with GPU and data-layout caveats.

How do I install Training Data in Claude Code?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill training-data -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/robust-video-matting/sub-skills/training-data in VectorSpaceLab/AREX-Skill) into .claude/skills/training-data in your project. Claude Code loads it when a task matches its description.

How do I install Training Data in Codex?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill training-data -a codex`. Or copy the skill folder (skills/repositories/repo-skills/robust-video-matting/sub-skills/training-data in VectorSpaceLab/AREX-Skill) into .agents/skills/training-data in your project. Codex loads it when a task matches its description.

Can I use Training Data 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-data -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-data, .gemini/skills/training-data, .github/skills/training-data and .opencode/skills/training-data in your project.

What does Training Data need to run?

Going by SKILL.md and its folder, Training Data needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Training Data 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 Data 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 Data use?

Training Data is published under the GPL-3.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Training Data use?

About 1k tokens (SKILL.md is roughly 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 4k tokens, read only when the agent opens those files.

What are the alternatives to Training Data?

Skills that share tags, products or a category with Training Data: Dataset Curation (wshobson/agents, 40k stars), Ito Training (affaan-m/ECC, 276k stars), Ray Train Distributed Training (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Datasets (Arize-ai/phoenix, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Training Data?

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.