Resume Tailor
reactive-resume/reactive-resume
Tailors one resume to one specific job posting. An agent skill from reactive-resume/reactive-resume.
Generate, validate, drop-test, resume, and batch-process rigid USD assets from reconstructed object meshes or exported HOI sequences in this repository.
Custom licenceAuto-check passed
Install Mesh To Usd Run
$ npx skills add nvidia-isaac/video_to_data --skill mesh-to-usd-run -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install nvidia-isaac/video_to_data mesh-to-usd-run --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/.codex/skills/mesh-to-usd-run .claude/skills/mesh-to-usd-run && 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 "mesh-to-usd-run" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.codex/skills/mesh-to-usd-run into .claude/skills/mesh-to-usd-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-to-usd-run", 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/.codex/skills/mesh-to-usd-runType 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 mesh-to-usd-run -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install nvidia-isaac/video_to_data mesh-to-usd-run --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/.codex/skills/mesh-to-usd-run .agents/skills/mesh-to-usd-run && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mesh-to-usd-run" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.codex/skills/mesh-to-usd-run into .agents/skills/mesh-to-usd-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-to-usd-run", 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 mesh-to-usd-run -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install nvidia-isaac/video_to_data mesh-to-usd-run --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/.codex/skills/mesh-to-usd-run .cursor/skills/mesh-to-usd-run && 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 "mesh-to-usd-run" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.codex/skills/mesh-to-usd-run into .cursor/skills/mesh-to-usd-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-to-usd-run", 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 .codex/skills/mesh-to-usd-run--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 mesh-to-usd-run -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install nvidia-isaac/video_to_data mesh-to-usd-run --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/.codex/skills/mesh-to-usd-run .gemini/skills/mesh-to-usd-run && 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 "mesh-to-usd-run" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.codex/skills/mesh-to-usd-run into .gemini/skills/mesh-to-usd-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-to-usd-run", 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 mesh-to-usd-runInstalls 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 mesh-to-usd-run -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/.codex/skills/mesh-to-usd-run .github/skills/mesh-to-usd-run && 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 "mesh-to-usd-run" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.codex/skills/mesh-to-usd-run into .github/skills/mesh-to-usd-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-to-usd-run", 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 mesh-to-usd-run -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 mesh-to-usd-run --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/.codex/skills/mesh-to-usd-run .opencode/skills/mesh-to-usd-run && 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 "mesh-to-usd-run" agent skill from https://github.com/nvidia-isaac/video_to_data/tree/main/.codex/skills/mesh-to-usd-run into .opencode/skills/mesh-to-usd-run/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mesh-to-usd-run", 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
mesh-to-usd-run- GitHub stars
- 861
- Token cost
- ~2k tokens
- SKILL.md length
- 731 words
- Files
- 2
- Skills in repo
- 13
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Generate, validate, drop-test, resume, and batch-process rigid USD assets from reconstructed object meshes or exported HOI sequences in this repository.
- A user asks to convert a GLB to USD
- SKILL.md covers Resolve the normal input…, Preflight and continue, Choose exactly one preparation… and Run a catalog batch deliberately, plus 2 more sections
- Calls python and git
- Create a physics-enabled rigid object
What it does
Mesh To Usd Run is an agent skill from nvidia-isaac/video_to_data. Generate, validate, drop-test, resume, and batch-process rigid USD assets from reconstructed object meshes or exported HOI sequences in this repository. Use when a user asks to convert a GLB to USD, create a physics-enabled rigid object, infer a recorded support pose, run an Isaac Sim hold-and-drop test, process a catalog of Einstar, BundleSDF, or SAM3D meshes, resume a partial job, or verify the generated USD, reports, and MP4 evidence.
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).
The repository describes itself as: Nvidia Isaac Video to Data Pipeline.
When your agent uses it
- A user asks to convert a GLB to USD
- Create a physics-enabled rigid object
- Infer a recorded support pose
- Run an Isaac Sim hold-and-drop test
Example prompts
- “/mesh-to-usd-run”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 193382a. 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:
pythongit
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, 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
Mesh To Usd Run loads about 2k tokens when it runs. Until then it costs about 114 tokens; SKILL.md has 731 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 731 words (~2,019 tokens).
“Use the top-level host wrappers from reconstruction/. Unless the user asks for commands only, run the workflow, monitor it, and verify both machine-readable reports and visual evidence.”
- name
- mesh-to-usd-run
Files
SKILL.md and 1 other file in .codex/skills/mesh-to-usd-run of nvidia-isaac/video_to_data.
- SKILL.md
- agents/openai.yaml
Open the folder on GitHubat commit 193382a
Compare with similar skills
Mesh To Usd Run 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 |
|---|---|---|---|---|---|---|
| Mesh To Usd Run this skillnvidia-isaac/video_to_data | 861 | — | ~2k | Automated safety check: Pass | Custom licence | |
| Resume Tailorreactive-resume/reactive-resume | 44k | — | ~8.3k | Automated safety check: Pass | MIT | |
| Batch Resume ScreenerLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.3k | Automated safety check: Pass | MIT | |
| Batchasgeirtj/system_prompts_leaks | 69k | — | ~1.3k | Automated safety check: Pass | CC0-1.0 | |
| Resume Bullet Writerreactive-resume/reactive-resume | 44k | — | ~8k | Automated safety check: Pass | MIT | |
| Reactive Resume Builderreactive-resume/reactive-resume | 44k | — | ~2k | Automated safety check: Pass | MIT |
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Questions about Mesh To Usd Run
What does Mesh To Usd Run do?
Generate, validate, drop-test, resume, and batch-process rigid USD assets from reconstructed object meshes or exported HOI sequences in this repository. Mesh To Usd Run is an agent skill from nvidia-isaac/video_to_data. Generate, validate, drop-test, resume, and batch-process rigid USD assets from reconstructed object meshes or exported HOI sequences in this repository.
When should I use Mesh To Usd Run?
Mesh To Usd Run fits situations like: A user asks to convert a GLB to USD; create a physics-enabled rigid object; infer a recorded support pose; run an Isaac Sim hold-and-drop test.
How do I install Mesh To Usd Run in Claude Code?
Run `npx skills add nvidia-isaac/video_to_data --skill mesh-to-usd-run -a claude-code`. Or copy the skill folder (.codex/skills/mesh-to-usd-run in nvidia-isaac/video_to_data) into .claude/skills/mesh-to-usd-run in your project. Claude Code loads it when a task matches its description.
How do I install Mesh To Usd Run in Codex?
Run `npx skills add nvidia-isaac/video_to_data --skill mesh-to-usd-run -a codex`. Or copy the skill folder (.codex/skills/mesh-to-usd-run in nvidia-isaac/video_to_data) into .agents/skills/mesh-to-usd-run in your project. Codex loads it when a task matches its description.
Can I use Mesh To Usd Run 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 mesh-to-usd-run -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mesh-to-usd-run, .gemini/skills/mesh-to-usd-run, .github/skills/mesh-to-usd-run and .opencode/skills/mesh-to-usd-run in your project.
What does Mesh To Usd Run need to run?
Going by SKILL.md and its folder, Mesh To Usd Run needs the command-line tools its instructions call (python and git). Our summary lists: Python 3.
Does Mesh To Usd Run access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Mesh To Usd Run 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 Mesh To Usd Run use?
Mesh To Usd Run 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 Mesh To Usd Run use?
About 2k tokens (SKILL.md is roughly 8.1k 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 Mesh To Usd Run?
Skills that share tags, products or a category with Mesh To Usd Run: Resume Tailor (reactive-resume/reactive-resume, 44k stars), Batch Resume Screener (LeoYeAI/openclaw-master-skills, 2.2k stars), Batch (asgeirtj/system_prompts_leaks, 69k stars) and Resume Bullet Writer (reactive-resume/reactive-resume, 44k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mesh To Usd Run?
nvidia-isaac (a GitHub organization) maintains it in nvidia-isaac/video_to_data, which has 861 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.