Dummy Dataset Generator
phuryn/pm-skills
Generates realistic test datasets with custom columns, row counts and business constraints, output as CSV, JSON, SQL inserts or a runnable Python script.
Guide for generating robot-behaviour datasets from trained Sharpa (floating-hand) V2D policies — rolling out checkpoints in Isaac Lab with cameras and exporting to LeRobot.
Custom licenceAuto-check passed
Install Sharpa Datagen
$ npx skills add nvidia-isaac/video_to_data --skill sharpa-datagen -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install nvidia-isaac/video_to_data sharpa-datagen --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/nvidia-isaac/video_to_data.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/sharpa-datagen .claude/skills/sharpa-datagen && 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 "sharpa-datagen" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.claude/skills/sharpa-datagen into .claude/skills/sharpa-datagen/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sharpa-datagen", 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/nvidia-isaac/video_to_data/tree/main/.claude/skills/sharpa-datagenType 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 nvidia-isaac/video_to_data --skill sharpa-datagen -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install nvidia-isaac/video_to_data sharpa-datagen --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nvidia-isaac/video_to_data.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/sharpa-datagen .agents/skills/sharpa-datagen && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sharpa-datagen" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.claude/skills/sharpa-datagen into .agents/skills/sharpa-datagen/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sharpa-datagen", 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 nvidia-isaac/video_to_data --skill sharpa-datagen -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install nvidia-isaac/video_to_data sharpa-datagen --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nvidia-isaac/video_to_data.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/sharpa-datagen .cursor/skills/sharpa-datagen && 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 "sharpa-datagen" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.claude/skills/sharpa-datagen into .cursor/skills/sharpa-datagen/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sharpa-datagen", 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/nvidia-isaac/video_to_data.git --path .claude/skills/sharpa-datagen--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 nvidia-isaac/video_to_data --skill sharpa-datagen -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install nvidia-isaac/video_to_data sharpa-datagen --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nvidia-isaac/video_to_data.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/sharpa-datagen .gemini/skills/sharpa-datagen && 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 "sharpa-datagen" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.claude/skills/sharpa-datagen into .gemini/skills/sharpa-datagen/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sharpa-datagen", 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 nvidia-isaac/video_to_data sharpa-datagenInstalls 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 nvidia-isaac/video_to_data --skill sharpa-datagen -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/nvidia-isaac/video_to_data.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/sharpa-datagen .github/skills/sharpa-datagen && 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 "sharpa-datagen" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.claude/skills/sharpa-datagen into .github/skills/sharpa-datagen/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sharpa-datagen", 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 nvidia-isaac/video_to_data --skill sharpa-datagen -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install nvidia-isaac/video_to_data sharpa-datagen --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nvidia-isaac/video_to_data.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/sharpa-datagen .opencode/skills/sharpa-datagen && 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 "sharpa-datagen" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.claude/skills/sharpa-datagen into .opencode/skills/sharpa-datagen/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sharpa-datagen", 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
sharpa-datagen- GitHub stars
- 850
- Token cost
- ~4.1k tokens
- SKILL.md length
- 1,766 words
- Files
- 1
- Skills in repo
- 13
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Guide for generating robot-behaviour datasets from trained Sharpa (floating-hand) V2D policies — rolling out checkpoints in Isaac Lab with cameras and exporting to LeRobot.
- Works in 11 steps: TL;DR happy path (one sequence) → Prerequisites (check FIRST) → Data requirements per record run (the #1… → …
- The user wants to generate/record a dataset from a trained policy
- SKILL.md covers 0. TL;DR happy path (one…, 1. Prerequisites (check FIRST), 2. Data requirements per… and 3. Running a record…, plus 10 more sections
- Calls docker and python; needs CSS_ACCESS_KEY and CSS_SECRET_KEY
What it does
Sharpa Datagen is an agent skill from nvidia-isaac/video_to_data. Guide for generating robot-behaviour datasets from trained Sharpa (floating-hand) V2D policies — rolling out checkpoints in Isaac Lab with cameras and exporting to LeRobot. Use this skill whenever the user wants to generate/record a dataset from a trained policy, run recorddataset.py / the Sharpa-V2D-Record-v0 task, batch-generate data across a dataset's checkpoints, visualize recorded rollouts, pull retargeted motion + support surfaces from CSS for a record run, or debug why recorded episodes fail (objects…
Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: Nvidia Isaac Video to Data Pipeline.
When your agent uses it
- The user wants to generate/record a dataset from a trained policy
- Run recorddataset.py / the Sharpa-V2D-Record-v0 task
- Batch-generate data across a datasets checkpoints
- Visualize recorded rollouts
Example prompts
- “data generation”
- “datagen”
- “record rollouts”
- “/sharpa-datagen”
Requirements
- Python 3
- Docker
- A credential in CSS_ACCESS_KEY
- A credential in CSS_SECRET_KEY
Workflow steps
11 steps, taken from the step headings in SKILL.md.
- TL;DR happy path (one sequence)
- Prerequisites (check FIRST)
- Data requirements per record run (the #1 failure class)
- Running a record (record_dataset.py)
- VOC (virtual object control) semantics — READ THIS
- Replay mode (validate trajectories without a policy)
- Metrics + the success-flag caveat
- Batch generation
- Visualization
- Operational gotchas
- Troubleshooting (symptom → cause)
What it can do on your machine
Read from SKILL.md and the folder at commit 12c36fb. 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:
dockerpython
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use docker, 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 these keys or tokens, usually read from environment variables:
CSS_ACCESS_KEYCSS_SECRET_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Sharpa Datagen loads about 4.1k tokens when it runs. Until then it costs about 193 tokens; SKILL.md has 1,766 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
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 1,766 words (~4,085 tokens).
“Roll out a trained per-sequence Sharpa policy in Isaac Lab, record camera + state/action observations, and export a LeRobot v3 dataset. All work runs inside the container, from /workspace/video_to_data/robotic_grounding.”
- name
- sharpa-datagen
Files
Just SKILL.md in .claude/skills/sharpa-datagen of nvidia-isaac/video_to_data.
Open the folder on GitHubat commit 12c36fb
Compare with similar skills
Sharpa Datagen 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 |
|---|---|---|---|---|---|---|
| Sharpa Datagen this skillnvidia-isaac/video_to_data | 850 | — | ~4.1k | Automated safety check: Pass | Custom licence | |
| Dummy Dataset Generatorphuryn/pm-skills | 27k | — | ~983 | Automated safety check: Pass | MIT | |
| Fal Trainnexu-io/open-design | 100k | — | ~293 | Automated safety check: Pass | Apache-2.0 | |
| Generatealirezarezvani/claude-skills | 28k | 1 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Fal Generatenexu-io/open-design | 100k | — | ~306 | Automated safety check: Pass | Apache-2.0 | |
| Video Generationbytedance/deer-flow | 83k | 4 repos | ~1.4k | Automated safety check: Pass | MIT |
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Questions about Sharpa Datagen
What does Sharpa Datagen do?
Guide for generating robot-behaviour datasets from trained Sharpa (floating-hand) V2D policies — rolling out checkpoints in Isaac Lab with cameras and exporting to LeRobot. Sharpa Datagen is an agent skill from nvidia-isaac/video_to_data. Guide for generating robot-behaviour datasets from trained Sharpa (floating-hand) V2D policies — rolling out checkpoints in Isaac Lab with cameras and exporting to LeRobot.
When should I use Sharpa Datagen?
Sharpa Datagen fits situations like: the user wants to generate/record a dataset from a trained policy; run recorddataset.py / the Sharpa-V2D-Record-v0 task; batch-generate data across a datasets checkpoints; visualize recorded rollouts.
How do I install Sharpa Datagen in Claude Code?
Run `npx skills add nvidia-isaac/video_to_data --skill sharpa-datagen -a claude-code`. Or copy the skill folder (.claude/skills/sharpa-datagen in nvidia-isaac/video_to_data) into .claude/skills/sharpa-datagen in your project. Claude Code loads it when a task matches its description.
How do I install Sharpa Datagen in Codex?
Run `npx skills add nvidia-isaac/video_to_data --skill sharpa-datagen -a codex`. Or copy the skill folder (.claude/skills/sharpa-datagen in nvidia-isaac/video_to_data) into .agents/skills/sharpa-datagen in your project. Codex loads it when a task matches its description.
Can I use Sharpa Datagen 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 nvidia-isaac/video_to_data --skill sharpa-datagen -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sharpa-datagen, .gemini/skills/sharpa-datagen, .github/skills/sharpa-datagen and .opencode/skills/sharpa-datagen in your project.
What does Sharpa Datagen need to run?
Going by SKILL.md and its folder, Sharpa Datagen needs the command-line tools its instructions call (docker and python) and credentials named CSS_ACCESS_KEY and CSS_SECRET_KEY. Our summary lists: Python 3; Docker; A credential in CSS_ACCESS_KEY; A credential in CSS_SECRET_KEY.
Does Sharpa Datagen access the network?
SKILL.md contains no URLs. Its commands use docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Sharpa Datagen 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 Sharpa Datagen use?
Sharpa Datagen has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does Sharpa Datagen use?
About 4.1k tokens (SKILL.md is roughly 16k 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 Sharpa Datagen?
Skills that share tags, products or a category with Sharpa Datagen: Dummy Dataset Generator (phuryn/pm-skills, 27k stars), Fal Train (nexu-io/open-design, 100k stars), Generate (alirezarezvani/claude-skills, 28k stars) and Fal Generate (nexu-io/open-design, 100k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sharpa Datagen?
nvidia-isaac (a GitHub organization) maintains it in nvidia-isaac/video_to_data, which has 850 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on October 8, 2026.
Source: nvidia-isaac/video_to_data on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.