LangBot Plugin Development
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Agent skill
Physicalai Train Working With Datasets
by open-edge-platform in open-edge-platform/physical-ai-studio
Works with Physical AI Studio datasets and Lightning datamodules built on the LeRobot format.
Install Physicalai Train Working With Datasets
$ npx skills add open-edge-platform/physical-ai-studio --skill physicalai-train-working-with-datasets -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install open-edge-platform/physical-ai-studio physicalai-train-working-with-datasets --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/open-edge-platform/physical-ai-studio.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/library/physicalai-train-working-with-datasets .claude/skills/physicalai-train-working-with-datasets && 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 "physicalai-train-working-with-datasets" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-working-with-datasets into .claude/skills/physicalai-train-working-with-datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-working-with-datasets", 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/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-working-with-datasetsType 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 open-edge-platform/physical-ai-studio --skill physicalai-train-working-with-datasets -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install open-edge-platform/physical-ai-studio physicalai-train-working-with-datasets --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/physical-ai-studio.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/library/physicalai-train-working-with-datasets .agents/skills/physicalai-train-working-with-datasets && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "physicalai-train-working-with-datasets" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-working-with-datasets into .agents/skills/physicalai-train-working-with-datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-working-with-datasets", 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 open-edge-platform/physical-ai-studio --skill physicalai-train-working-with-datasets -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install open-edge-platform/physical-ai-studio physicalai-train-working-with-datasets --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/physical-ai-studio.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/library/physicalai-train-working-with-datasets .cursor/skills/physicalai-train-working-with-datasets && 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 "physicalai-train-working-with-datasets" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-working-with-datasets into .cursor/skills/physicalai-train-working-with-datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-working-with-datasets", 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/open-edge-platform/physical-ai-studio.git --path skills/library/physicalai-train-working-with-datasets--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 open-edge-platform/physical-ai-studio --skill physicalai-train-working-with-datasets -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install open-edge-platform/physical-ai-studio physicalai-train-working-with-datasets --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/physical-ai-studio.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/library/physicalai-train-working-with-datasets .gemini/skills/physicalai-train-working-with-datasets && 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 "physicalai-train-working-with-datasets" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-working-with-datasets into .gemini/skills/physicalai-train-working-with-datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-working-with-datasets", 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 open-edge-platform/physical-ai-studio physicalai-train-working-with-datasetsInstalls 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 open-edge-platform/physical-ai-studio --skill physicalai-train-working-with-datasets -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/open-edge-platform/physical-ai-studio.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/library/physicalai-train-working-with-datasets .github/skills/physicalai-train-working-with-datasets && 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 "physicalai-train-working-with-datasets" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-working-with-datasets into .github/skills/physicalai-train-working-with-datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-working-with-datasets", 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 open-edge-platform/physical-ai-studio --skill physicalai-train-working-with-datasets -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install open-edge-platform/physical-ai-studio physicalai-train-working-with-datasets --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/physical-ai-studio.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/library/physicalai-train-working-with-datasets .opencode/skills/physicalai-train-working-with-datasets && 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 "physicalai-train-working-with-datasets" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-working-with-datasets into .opencode/skills/physicalai-train-working-with-datasets/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-working-with-datasets", 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
physicalai-train-working-with-datasets- GitHub stars
- 131
- Token cost
- ~1.2k tokens
- SKILL.md length
- 430 words
- Files
- 1
- Skills in repo
- 7
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Works with Physical AI Studio datasets and Lightning datamodules built on the LeRobot format.
- Works in 5 steps: Pick the dataset by repo_id and confirm… → Verify a batch through the Python API… → Verify CLI parity when the dataset is… → …
- Wiring physicalai.data.lerobot.LeRobotDataModule into a training config
- SKILL.md covers Python API usage, Wiring data into a training…, Workflow and Debugging dataloading, plus 3 more sections
- Calls uv
What it does
Physicalai Train Working With Datasets is an agent skill from open-edge-platform/physical-ai-studio. Works with Physical AI Studio datasets and Lightning datamodules built on the LeRobot format. Use when wiring physicalai.data.lerobot.LeRobotDataModule into a training config, choosing a repoid, converting between the physicalai and lerobot data layouts, defining observation Features/FeatureType, setting normalization, or debugging batch shapes and dataloading.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Development, covering Database schema design and Debugging. It works with Python. The repository describes itself as: Physical AI Studio is an end-to-end framework for training robots to perform tasks through imitation learning from human demonstrations. The licence is Apache-2.0.
When your agent uses it
- Wiring physicalai.data.lerobot.LeRobotDataModule into a training config
- Choosing a repoid
- Converting between the physicalai and lerobot data layouts
- Defining observation Features/FeatureType
Example prompts
- “Use the physicalai-train-working-with-datasets skill to work with Physical AI Studio datasets and Lightning datamodules built on the LeRobot format”
- “/physicalai-train-working-with-datasets”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Pick the dataset by repo_id and confirm its features (image keys, state dim, action dim) match the target policy's Config.
- Verify a batch through the Python API before training
- Verify CLI parity when the dataset is configured through YAML
- Convert layouts only when needed via converters.py (DataFormat.physicalai ↔ DataFormat.lerobot); keep field names stable, since they…
- Set normalization through NormalizationParameters/Feature consistently with what the policy expects at inference.
What it can do on your machine
Read from SKILL.md and the folder at commit 429ffd4. 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
Shell commands in SKILL.md call:
uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
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
Physicalai Train Working With Datasets loads about 1.2k tokens when it runs. Until then it costs about 101 tokens; SKILL.md has 430 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from open-edge-platform/physical-ai-studio at commit 429ffd4, republished under its Apache-2.0 licence (© open-edge-platform). 430 words, ~1,201 tokens.
Download SKILL.mdSave it as
.claude/skills/physicalai-train-working-with-datasets/SKILL.md (or your agent's skills folder).- name
- physicalai-train-working-with-datasets
- description
- Works with Physical AI Studio datasets and Lightning datamodules built on the LeRobot format. Use when wiring physicalai.data.lerobot.LeRobotDataModule into a training config, choosing a repo_id, converting between the physicalai and lerobot data layouts, defining observation Features/FeatureType, setting normalization, or debugging batch shapes and dataloading.
- license
- Apache-2.0
Working with Studio Datasets
Studio data lives in library/src/physicalai/data/. Datasets use the LeRobot format and are consumed through Lightning datamodules. The datamodules are first-class Python API objects; YAML/CLI configs are a serialization of the same construction path.
Key modules:
data/lerobot/datamodule.py—LeRobotDataModule(the class configs reference asphysicalai.data.lerobot.LeRobotDataModule).data/lerobot/dataset.py— LeRobot dataset wrapper.data/lerobot/converters.py—DataFormat(StrEnum:physicalai,lerobot) and bidirectional field mapping between the two layouts.data/observation.py—Observation,Feature,FeatureType,NormalizationParameters.data/datamodules.py— baseDataModule(LightningLightningDataModule, auto num-workers heuristic).data/dataset.py— baseDataset;data/gym.py—GymDatasetfor gym-generated data.
Python API usage
Use this path for notebooks, tests, direct batch inspection, or debugging dataloading without involving the training CLI.
from physicalai.data import LeRobotDataModule
datamodule = LeRobotDataModule(repo_id="lerobot/pusht", train_batch_size=2)
datamodule.prepare_data()
datamodule.setup("fit")
batch = next(iter(datamodule.train_dataloader()))Done when: the batch contains the observation/action fields the policy expects, with the expected batch/action dimensions.
Wiring data into a training config
In a physicalai fit config, the data block selects the datamodule and its repo_id:
data:
class_path: physicalai.data.lerobot.LeRobotDataModule
init_args:
repo_id: lerobot/pusht
train_batch_size: 64repo_id points at a LeRobot/HuggingFace dataset; the datamodule pulls it on first use. See the physicalai-train-training-a-policy skill for the full config.
Workflow
- Pick the dataset by
repo_idand confirm its features (image keys, state dim, action dim) match the target policy'sConfig.- Done when: the policy's expected
Featurenames and action dimension line up with the dataset.
- Done when: the policy's expected
- Verify a batch through the Python API before training:
python datamodule.prepare_data() datamodule.setup("fit") batch = next(iter(datamodule.train_dataloader()))- Done when: the batch has correct keys and shapes without invoking the CLI.
- Verify CLI parity when the dataset is configured through YAML:
bash physicalai fit --config <config.yaml> --trainer.fast_dev_run=true- Done when: one batch flows through with correct shapes and no missing-feature errors.
- Convert layouts only when needed via
converters.py(DataFormat.physicalai↔DataFormat.lerobot); keep field names stable, since they propagate to training and export. - Set normalization through
NormalizationParameters/Featureconsistently with what the policy expects at inference.
Show full SKILL.md (150 more words)Show less
Debugging dataloading
- Missing/renamed feature → the config's dataset features disagree with the policy; align
Featurenames indata/observation.pyconventions. - Slow/stalled first batch → the LeRobot
repo_idis downloading; expected on first run (see therequires_downloadtest marker for tests that need this). - Wrong batch dimensions → check
train_batch_sizeand the datamodule's collate/observation handling before changing the policy. - OOM or heavy swapping during training (common on smaller policies like ACT/SmolVLA on low-RAM machines) → try
pin_memory=Falseand/orpersistent_workers=Falseon theDataModule; seelibrary/docs/explanation/data/datamodules.md.
Required checks
- Feature names,
FeatureType, action dim, and normalization match between dataset,Config, and any export metadata. - Conversions round-trip without dropping or renaming fields.
- Direct datamodule API construction and YAML config construction produce compatible batches.
- Tests that require downloads are marked
requires_download; keep defaultuv run --no-sync pytestrunnable offline.
Verify
# from library/
uv run --no-sync pytest tests/unit/data tests/unit/datamodulesRelated skills
physicalai-train-training-a-policy— thedatablock is one half of a training config.physicalai-train-adding-a-policy— align observation features with the policyConfig.
© open-edge-platform, 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
Just SKILL.md in skills/library/physicalai-train-working-with-datasets of open-edge-platform/physical-ai-studio.
Open the folder on GitHubat commit 429ffd4
Compare with similar skills
Physicalai Train Working With Datasets 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 |
|---|---|---|---|---|---|---|
| Physicalai Train Working With Datasets this skillopen-edge-platform/physical-ai-studio | 131 | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| LangBot Plugin Developmentlangbot-app/LangBot | 18k | — | ~3.9k | Automated safety check: Pass | Apache-2.0 | |
| Python Performance Optimizationwshobson/agents | 40k | 13 repos | ~814 | Automated safety check: Pass | MIT | |
| Git History Bug Auditben-manes/caffeine | 18k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| Keybase RPC Log Analysiskeybase/client | 9.3k | — | ~3k | Automated safety check: Pass | BSD-3-Clause | |
| The Art of Debuggingstas00/the-art-of-debugging | 1.7k | — | ~6.1k | Automated safety check: Notes | CC-BY-SA-4.0 |
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Related
Works with
Categories
Questions about Physicalai Train Working With Datasets
What does Physicalai Train Working With Datasets do?
Works with Physical AI Studio datasets and Lightning datamodules built on the LeRobot format. Physicalai Train Working With Datasets is an agent skill from open-edge-platform/physical-ai-studio. Works with Physical AI Studio datasets and Lightning datamodules built on the LeRobot format.
When should I use Physicalai Train Working With Datasets?
Physicalai Train Working With Datasets fits situations like: wiring physicalai.data.lerobot.LeRobotDataModule into a training config; choosing a repoid; converting between the physicalai and lerobot data layouts; defining observation Features/FeatureType.
How do I install Physicalai Train Working With Datasets in Claude Code?
Run `npx skills add open-edge-platform/physical-ai-studio --skill physicalai-train-working-with-datasets -a claude-code`. Or copy the skill folder (skills/library/physicalai-train-working-with-datasets in open-edge-platform/physical-ai-studio) into .claude/skills/physicalai-train-working-with-datasets in your project. Claude Code loads it when a task matches its description.
How do I install Physicalai Train Working With Datasets in Codex?
Run `npx skills add open-edge-platform/physical-ai-studio --skill physicalai-train-working-with-datasets -a codex`. Or copy the skill folder (skills/library/physicalai-train-working-with-datasets in open-edge-platform/physical-ai-studio) into .agents/skills/physicalai-train-working-with-datasets in your project. Codex loads it when a task matches its description.
Can I use Physicalai Train Working With Datasets 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 open-edge-platform/physical-ai-studio --skill physicalai-train-working-with-datasets -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/physicalai-train-working-with-datasets, .gemini/skills/physicalai-train-working-with-datasets, .github/skills/physicalai-train-working-with-datasets and .opencode/skills/physicalai-train-working-with-datasets in your project.
What does Physicalai Train Working With Datasets need to run?
Going by SKILL.md and its folder, Physicalai Train Working With Datasets needs the command-line tools its instructions call (uv). Our summary lists: Python 3.
Does Physicalai Train Working With Datasets access the network?
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Physicalai Train Working With Datasets 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. Review the folder before installing.
What licence does Physicalai Train Working With Datasets use?
Physicalai Train Working With Datasets 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 Physicalai Train Working With Datasets use?
About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Physicalai Train Working With Datasets?
Skills that share tags, products or a category with Physicalai Train Working With Datasets: LangBot Plugin Development (langbot-app/LangBot, 18k stars), Python Performance Optimization (wshobson/agents, 40k stars), Git History Bug Audit (ben-manes/caffeine, 18k stars) and Keybase RPC Log Analysis (keybase/client, 9.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Physicalai Train Working With Datasets?
open-edge-platform (a GitHub organization) maintains it in open-edge-platform/physical-ai-studio, which has 131 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on October 9, 2026.
Source: open-edge-platform/physical-ai-studio on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.