Agent skill

Run Data Collection

Launch a datacollection automated trajectory-collection task on a GPU host using the geniesim autocollect run CLI verb (which wraps scripts/rundatacollection.sh: docker run -d + in-container…

MPL-2.0Auto-check: notesDevOps & Cloud

Install Run Data Collection

skills CLI

$ npx skills add AgibotTech/genie_sim --skill run-data-collection -a claude-code

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

GitHub CLI

$ gh skill install AgibotTech/genie_sim run-data-collection --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/AgibotTech/genie_sim.git skills-src && mkdir -p .claude/skills && cp -r skills-src/source/data_collection/skills/run-data-collection .claude/skills/run-data-collection && 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/

Paste into Claude Code

Install the "run-data-collection" agent skill from https://github.com/AgibotTech/genie_sim/tree/main/source/data_collection/skills/run-data-collection into .claude/skills/run-data-collection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-data-collection", 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

$skill-installer install https://github.com/AgibotTech/genie_sim/tree/main/source/data_collection/skills/run-data-collection

Type 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.

skills CLI

$ npx skills add AgibotTech/genie_sim --skill run-data-collection -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install AgibotTech/genie_sim run-data-collection --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/AgibotTech/genie_sim.git skills-src && mkdir -p .agents/skills && cp -r skills-src/source/data_collection/skills/run-data-collection .agents/skills/run-data-collection && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "run-data-collection" agent skill from https://github.com/AgibotTech/genie_sim/tree/main/source/data_collection/skills/run-data-collection into .agents/skills/run-data-collection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-data-collection", 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.

skills CLI

$ npx skills add AgibotTech/genie_sim --skill run-data-collection -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install AgibotTech/genie_sim run-data-collection --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/AgibotTech/genie_sim.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/source/data_collection/skills/run-data-collection .cursor/skills/run-data-collection && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "run-data-collection" agent skill from https://github.com/AgibotTech/genie_sim/tree/main/source/data_collection/skills/run-data-collection into .cursor/skills/run-data-collection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-data-collection", 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

$ gemini skills install https://github.com/AgibotTech/genie_sim.git --path source/data_collection/skills/run-data-collection

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add AgibotTech/genie_sim --skill run-data-collection -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install AgibotTech/genie_sim run-data-collection --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/AgibotTech/genie_sim.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/source/data_collection/skills/run-data-collection .gemini/skills/run-data-collection && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "run-data-collection" agent skill from https://github.com/AgibotTech/genie_sim/tree/main/source/data_collection/skills/run-data-collection into .gemini/skills/run-data-collection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-data-collection", 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.

GitHub CLI

$ gh skill install AgibotTech/genie_sim run-data-collection

Installs 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).

skills CLI

$ npx skills add AgibotTech/genie_sim --skill run-data-collection -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/AgibotTech/genie_sim.git skills-src && mkdir -p .github/skills && cp -r skills-src/source/data_collection/skills/run-data-collection .github/skills/run-data-collection && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "run-data-collection" agent skill from https://github.com/AgibotTech/genie_sim/tree/main/source/data_collection/skills/run-data-collection into .github/skills/run-data-collection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-data-collection", 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.

skills CLI

$ npx skills add AgibotTech/genie_sim --skill run-data-collection -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install AgibotTech/genie_sim run-data-collection --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/AgibotTech/genie_sim.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/source/data_collection/skills/run-data-collection .opencode/skills/run-data-collection && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "run-data-collection" agent skill from https://github.com/AgibotTech/genie_sim/tree/main/source/data_collection/skills/run-data-collection into .opencode/skills/run-data-collection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-data-collection", 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: run-data-collection
GitHub stars: 1.4k
Token cost: ~1.1k tokens
SKILL.md length: 336 words
Files: 1
Skills in repo: 26
Repo updated: Sep 7, 2026
First seen: Oct 7, 2026
Licence: MPL-2.0

At a glance

Launch a datacollection automated trajectory-collection task on a GPU host using the geniesim autocollect run CLI verb (which wraps scripts/rundatacollection.sh: docker run -d + in-container…

Works in 4 steps: Resolve the task → Check prerequisites → Launch → …
Run data collection
SKILL.md covers When to Use, Critical Patterns, Workflow and Notes
Calls docker, python3 and pip

What it does

Run Data Collection is an agent skill from AgibotTech/genie_sim. Launch a datacollection automated trajectory-collection task on a GPU host using the geniesim autocollect run CLI verb (which wraps scripts/rundatacollection.sh: docker run -d + in-container server+client). Trigger: when the user asks to "采集数据", "跑数据采集", "run data collection", "collect a task", "生产轨迹", "launch a tasks/geniesim2025/<....json", or wants to produce agibot-format episodes from a datacollection task template.

Its SKILL.md is about 1.1k 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 DevOps & Cloud, covering Containers. It works with Docker. The repository describes itself as: Simulation Platform from AgiBot. The licence is MPL-2.0.

When your agent uses it

Run data collection
Launch a tasks/geniesim2025/<....json
Wants to produce agibot-format episodes from a datacollection task template

Example prompts

“run data collection”
“collect a task”
“launch a tasks/geniesim2025/<....json”
“/run-data-collection”

Requirements

Python 3
Docker

Workflow steps

4 steps, taken from the step headings in SKILL.md.

Resolve the task
Check prerequisites
Launch
Monitor & verify

What it can do on your machine

Read from SKILL.md and the folder at commit 6ca11c7. 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:
- docker
- python3
- pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use docker and pip, 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

Run Data Collection loads about 1.1k tokens when it runs. Until then it costs about 113 tokens; SKILL.md has 336 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~113
When it runs · the whole SKILL.md, loaded when a task matches: ~1.1k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NoteRuns commands with sudoSKILL.md:40

preferring `sudo setfacl`, degrading to `chmod -R a+rwX` when sudo isn't

NoteRuns commands with sudoSKILL.md:68
```
Interactive terminal (sudo can prompt):
```

NoteRuns commands with sudoSKILL.md:76

when sudo is unavailable; no PTY trick needed):

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 AgibotTech/genie_sim at commit 6ca11c7, republished under its MPL-2.0 licence (© AgibotTech). 336 words, ~1,143 tokens.

Download SKILL.mdSave it as .claude/skills/run-data-collection/SKILL.md (or your agent's skills folder).

name: run-data-collection
description: Launch a data_collection automated trajectory-collection task on a GPU host using the `geniesim autocollect run` CLI verb (which wraps scripts/run_data_collection.sh: docker run -d + in-container server+client). Trigger: when the user asks to "采集数据", "跑数据采集", "run data collection", "collect a task", "生产轨迹", "launch a tasks/geniesim_2025/<...>.json", or wants to produce agibot-format episodes from a data_collection task template.
license: MPL-2.0
metadata.author: genie-sim
metadata.version: 1.0

When to Use

User wants to produce trajectory episodes from a data_collection task template on a workstation with Docker + an NVIDIA GPU.
User references a task under source/data_collection/tasks/.

Do not use for:

Running a benchmark/evaluation task → run-benchmark.
Just listing/inspecting tasks → geniesim autocollect list directly.

Critical Patterns

run is host-orchestrated, not an in-container exec. It shells out to scripts/run_data_collection.sh, which does docker run -d against geniesim3-data-collection:latest and the entrypoint launches two processes (Isaac Sim server + task client). Don't treat it like benchmark run.
Collect the inputs first: the task (basename / path / unique substring) and the run flags (--headless, --no-record, --standalone, --container-name). Use --dry-run to confirm resolution before launching.
Prerequisites: Docker + NVIDIA GPU; the image registry.agibot.com/genie-sim/geniesim3-data-collection:latest built/pulled; geniesim_assets pip-installed (editable) on the host — the CLI discovers it via find_spec and bind-mounts it at /geniesim_assets.
Unattended works. run_data_collection.sh grants uid 1234 access preferring sudo setfacl, degrading to chmod -R a+rwX when sudo isn't usable — so headless/background runs work without a tty. (The fallback world-writes the output dirs on the host.)
Confirm before launching. A real run spawns a GPU container, takes minutes, and writes ~1.5 GB per episode. Ask before kicking it off.

Workflow

Step 1 — Resolve the task

bash

geniesim autocollect list --robot=g2 <substr>     # discover
geniesim autocollect run <TASK> --headless --standalone --dry-run   # preview

--dry-run prints the resolved task path + the exact run_data_collection.sh command without launching. Disambiguate if it reports multiple matches.

Step 2 — Check prerequisites

bash

docker images | grep geniesim3-data-collection      # image present?
nvidia-smi                                             # GPU free?
python3 -c "import importlib.util as u; print('geniesim_assets OK' if u.find_spec('geniesim_assets') else 'NOT INSTALLED')"   # assets pkg editable-installed?

Step 3 — Launch

Interactive terminal (sudo can prompt):

bash

pip install -e /path/to/geniesim_assets   # once on the host (editable)
geniesim autocollect run <TASK> --headless --standalone

Unattended / detached (no tty) — works directly (the script degrades to chmod when sudo is unavailable; no PTY trick needed):

bash

cd <repo-root>
PYTHONPATH=source/geniesim_cli/src \
  nohup python3 -m geniesim_cli autocollect run <TASK> --headless --standalone \
  > /tmp/dc-run.log 2>&1 &

(Use python3 -m geniesim_cli … if the geniesim console script isn't on PATH.)

Step 4 — Monitor & verify

bash

tail -f source/data_collection/logs/<TASK>/data_collector_server.log   # Isaac Sim startup
tail -f source/data_collection/logs/<TASK>/run_data_collection.log     # stages / TASK SUCCESS / job done
docker ps | grep data_collection                                       # container up
ls source/data_collection/recording_data/                              # episodes landing

Success looks like job done in the client log, the container auto-removed (EXIT trap), and one recording_data/[{TASK}_{INDEX}]/ dir per episode with aligned_joints*.h5, observations/videos/*, state.json, data_info.json.

Notes

--no-record disables recording (drops --publish_ros + --use_recording); omit it to record.
Recording produces ~1.5 GB/episode — watch disk; clean recording_data/ after validating.
The container is ephemeral; only the mounted recording_data/, logs/, saved_task/ and the Isaac cache survive a run.
Full task-config authoring: source/data_collection/TASK_CONFIG_GUIDE.md. Module reference: source/data_collection/AGENTS.md.

Files

Just SKILL.md in source/data_collection/skills/run-data-collection of AgibotTech/genie_sim.

Open the folder on GitHubat commit 6ca11c7

Compare with similar skills

Run Data Collection 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.

Run Data Collection compared with similar skills
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Iron Proxy Gateway for NanoClawnanocoai/nanoclaw	31k	—	~4.6k	Automated safety check: Notes	MIT	yesterday
GreptimeDB Dev Docker ImageGreptimeTeam/greptimedb	6.7k	—	~4k	Automated safety check: Notes	Apache-2.0	today
Senior DevOps Toolkitmaslennikov-ig/claude-code-orchestrator-kit	260	6 repos	~1.1k	Automated safety check: Notes	Custom licence	7 mo ago
LangBot Deployment Guidelangbot-app/LangBot	18k	—	~1.2k	Automated safety check: Notes	Apache-2.0	today
Build Openshell Mxc WindowsNVIDIA/OpenShell	15k	—	~4.9k	Automated safety check: Pass	Apache-2.0	today

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Build Workspace
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Works with

Docker

Questions about Run Data Collection

What does Run Data Collection do?

Launch a datacollection automated trajectory-collection task on a GPU host using the geniesim autocollect run CLI verb (which wraps scripts/rundatacollection.sh: docker run -d + in-container…. Run Data Collection is an agent skill from AgibotTech/genie_sim.sh: docker run -d + in-container server+client).

When should I use Run Data Collection?

Run Data Collection fits situations like: run data collection; launch a tasks/geniesim2025/<....json; wants to produce agibot-format episodes from a datacollection task template.

How do I install Run Data Collection in Claude Code?

Run `npx skills add AgibotTech/genie_sim --skill run-data-collection -a claude-code`. Or copy the skill folder (source/data_collection/skills/run-data-collection in AgibotTech/genie_sim) into .claude/skills/run-data-collection in your project. Claude Code loads it when a task matches its description.

How do I install Run Data Collection in Codex?

Run `npx skills add AgibotTech/genie_sim --skill run-data-collection -a codex`. Or copy the skill folder (source/data_collection/skills/run-data-collection in AgibotTech/genie_sim) into .agents/skills/run-data-collection in your project. Codex loads it when a task matches its description.

Can I use Run Data Collection 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 AgibotTech/genie_sim --skill run-data-collection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/run-data-collection, .gemini/skills/run-data-collection, .github/skills/run-data-collection and .opencode/skills/run-data-collection in your project.

What does Run Data Collection need to run?

Going by SKILL.md and its folder, Run Data Collection needs the command-line tools its instructions call (docker, python3 and pip). Our summary lists: Python 3; Docker.

Does Run Data Collection access the network?

SKILL.md contains no URLs. Its commands use docker and pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Run Data Collection safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Run Data Collection use?

Run Data Collection is published under the MPL-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Run Data Collection use?

About 1.1k tokens (SKILL.md is roughly 4.6k 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 Run Data Collection?

Skills that share tags, products or a category with Run Data Collection: Iron Proxy Gateway for NanoClaw (nanocoai/nanoclaw, 31k stars), GreptimeDB Dev Docker Image (GreptimeTeam/greptimedb, 6.7k stars), Senior DevOps Toolkit (maslennikov-ig/claude-code-orchestrator-kit, 260 stars) and LangBot Deployment Guide (langbot-app/LangBot, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Run Data Collection?

AgibotTech (a GitHub organization) maintains it in AgibotTech/genie_sim, which has 1,414 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on September 7, 2026.

Source: AgibotTech/genie_sim on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Run Data Collection

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

When to Use

Critical Patterns

Workflow

Step 1 — Resolve the task

Step 2 — Check prerequisites

Step 3 — Launch

Step 4 — Monitor & verify

Notes

Files

Compare with similar skills

Similar skills

More from AgibotTech/genie_sim

Related

Questions about Run Data Collection