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A skill your agent uses when a user needs to get started with PAIDF Auto-Labeling, plan a scenario, run or debug a shipped cookbook, author prompts or cookbooks, migrate a pipeline, or configure a…
OfficialApache-2.0Auto-check passed
Install Paidf Auto Labeling
$ npx skills add NVIDIA/skills --skill paidf-auto-labeling -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills paidf-auto-labeling --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/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/paidf-auto-labeling .claude/skills/paidf-auto-labeling && 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 "paidf-auto-labeling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/paidf-auto-labeling into .claude/skills/paidf-auto-labeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paidf-auto-labeling", 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/skills/tree/main/skills/paidf-auto-labelingType 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/skills --skill paidf-auto-labeling -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills paidf-auto-labeling --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/paidf-auto-labeling .agents/skills/paidf-auto-labeling && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "paidf-auto-labeling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/paidf-auto-labeling into .agents/skills/paidf-auto-labeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paidf-auto-labeling", 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/skills --skill paidf-auto-labeling -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills paidf-auto-labeling --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/paidf-auto-labeling .cursor/skills/paidf-auto-labeling && 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 "paidf-auto-labeling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/paidf-auto-labeling into .cursor/skills/paidf-auto-labeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paidf-auto-labeling", 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/skills.git --path skills/paidf-auto-labeling--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/skills --skill paidf-auto-labeling -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills paidf-auto-labeling --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/paidf-auto-labeling .gemini/skills/paidf-auto-labeling && 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 "paidf-auto-labeling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/paidf-auto-labeling into .gemini/skills/paidf-auto-labeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paidf-auto-labeling", 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/skills paidf-auto-labelingInstalls 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/skills --skill paidf-auto-labeling -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/paidf-auto-labeling .github/skills/paidf-auto-labeling && 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 "paidf-auto-labeling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/paidf-auto-labeling into .github/skills/paidf-auto-labeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paidf-auto-labeling", 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/skills --skill paidf-auto-labeling -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/skills paidf-auto-labeling --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/paidf-auto-labeling .opencode/skills/paidf-auto-labeling && 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 "paidf-auto-labeling" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/paidf-auto-labeling into .opencode/skills/paidf-auto-labeling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "paidf-auto-labeling", 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
paidf-auto-labeling- GitHub stars
- 3.5k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 660 words
- Files
- 53 (incl. references)
- Skills in repo
- 386
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses when a user needs to get started with PAIDF Auto-Labeling, plan a scenario, run or debug a shipped cookbook, author prompts or cookbooks, migrate a pipeline, or configure a…
- Works in 8 steps: Confirm the critical run inputs with the… → Verify the environment: repository… → Run a shipped example first to confirm… → …
- A user needs to get started with PAIDF Auto-Labeling
- SKILL.md covers Routing (Read First), Instructions, Examples and Guardrails
- Calls make
What it does
Paidf Auto Labeling is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when a user needs to get started with PAIDF Auto-Labeling, plan a scenario, run or debug a shipped cookbook, author prompts or cookbooks, migrate a pipeline, or configure a stage. Confirm critical inputs (data path, output path, endpoints) and ask when any are missing. This is a router: read the matching reference instead of inventing a workflow.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 58 other files, including reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/README.md`).
The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
When your agent uses it
- A user needs to get started with PAIDF Auto-Labeling
- Plan a scenario
- Debug a shipped cookbook
- Migrate a pipeline
Example prompts
- “/paidf-auto-labeling”
Workflow steps
8 steps, taken from the first numbered list in SKILL.md.
- Confirm the critical run inputs with the user before doing anything else, and
- Verify the environment: repository cloned, make targets available, the
- Run a shipped example first to confirm the stack works end to end before
- Plan the target scenario: define modality, domain, intended consumer, and
- Adapt the closest shipped cookbook to the new domain rather than authoring
- Author the domain prompts and question banks. Use
- Configure the per-stage settings for the domain (detector classes or SAM3
- Dry-run the adapted cookbook, then execute and validate the outputs. Use
What it can do on your machine
Read from SKILL.md and the folder at commit dfdd080. 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:
make
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
Paidf Auto Labeling loads about 1.8k tokens when it runs, and up to ~47k if it reads all its reference files. Until then it costs about 93 tokens; SKILL.md has 660 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 NVIDIA/skills at commit dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 660 words, ~1,830 tokens.
Download SKILL.mdSave it as
.claude/skills/paidf-auto-labeling/SKILL.md (or your agent's skills folder). This skill also uses 52 other files; get the full folder from GitHub.- name
- paidf-auto-labeling
- description
- Use when a user needs to get started with PAIDF Auto-Labeling, plan a scenario, run or debug a shipped cookbook, author prompts or cookbooks, migrate a pipeline, or configure a stage. Confirm critical inputs (data path, output path, endpoints) and ask when any are missing. This is a router: read the matching reference instead of inventing a workflow.
- license
- Apache-2.0
- owner
- NVIDIA
- service
- physical-ai-data-factory
- version
- 1.1.0
- reviewed
- 2026-09-14
- author
- NVIDIA <opensource@nvidia.com>
- metadata.author
- NVIDIA <opensource@nvidia.com>
- metadata.tags
- getting-started, onboarding, new-domain, quickstart, new-use-case
PAIDF Auto-Labeling
Use this skill when a user wants to kick off PAIDF Auto-Labeling on their own data, domain, or use case, or when the request matches a shipped cookbook, stage, authoring, or migration task. This is a router: sequence the specialized references instead of duplicating their detail.
Routing (Read First)
| Request looks like | Read |
|---|---|
| New user, clean checkout, first validated run, "how do I get started" | This file, then the matching reference below |
| Choose annotation targets / stage subset for a domain | references/scenario-planning.md |
| Create, review, or adapt a cookbook | references/cookbook-authoring.md |
| Write or adapt VLM/LLM prompts or question banks | references/prompt-authoring.md |
| Migrate an existing annotation repo into this one | references/pipeline-migration.md |
| Run the video data augmentation cookbook | references/video-data-augmentation.md |
| Run or choose an EPAS / PAS cookbook | references/event-and-person-attribute-search.md |
| Run event-verification reasoning | references/event-verification-reasoning.md |
| Debug an already-integrated workflow | references/workflow-runner-debugging.md |
| Implement or review a new stage or Dockerized service | references/workflow-stage-integration.md |
| Configure or debug one production stage | The matching file under references/stages/ |
Stage references: super-resolution, detection-and-tracking, captioning, visual-qa, reasoning, person-attribute-search, grounding-2d, referring-expressions, training-export.
Instructions
- Confirm the critical run inputs with the user before doing anything else, and
ask a concise question whenever one is missing or ambiguous - never guess or
silently invent a default. At minimum confirm: input data path, output path,
VLM/LLM endpoint URLs and model names, model cache path, GPU ids, and (for
reasoning-capable models) the
max_tokenscap. Restate the confirmed values back to the user before the first execution. - Verify the environment: repository cloned,
maketargets available, the model cache path exists, the VLM/LLM endpoints are reachable, and a GPU is available. State any missing prerequisite as a blocker instead of assuming it. - Run a shipped example first to confirm the stack works end to end before customizing. Pick the closest operator pipeline - video data augmentation, event-and-person-attribute-search, or event-verification-reasoning - and run its committed cookbook. Use the matching operator reference.
- Plan the target scenario: define modality, domain, intended consumer, and required annotations, and get a minimal stage subset. Use scenario-planning.
- Adapt the closest shipped cookbook to the new domain rather than authoring from scratch. Use cookbook-authoring.
- Author the domain prompts and question banks. Use prompt-authoring.
- Configure the per-stage settings for the domain (detector classes or SAM3
prompts, endpoints, windowing,
max_tokens). Use the relevant stage reference, starting with detection-and-tracking. - Dry-run the adapted cookbook, then execute and validate the outputs. Use workflow-runner-debugging.
Adopting an existing external annotation or dataset-generation repository into PAIDF instead of starting from a shipped cookbook is a migration task; use pipeline-migration for that path.
Show full SKILL.md (243 more words)Show less
Examples
New user, new domain: "I cloned the repo and have my own warehouse-safety video. How do I produce auto-labels for my domain?"
Guided path:
- Confirm env (model cache, VLM/LLM endpoints, GPU), then prove the stack on a shipped example before customizing:
make run SCRIPT=workflow-runner:main \
ARGS='--cookbook-file cookbooks/video_data_augmentation/configs/pipeline_video.yaml --container-dry-run'- Plan the domain (scenario-planning) -> subset
detection_and_tracking -> captioning -> visual_qa -> reasoning -> training_export(addgrounding_2dfor caption→boxes orreferring_expressionsfor boxes→phrases; use grounding-2d / referring-expressions). - Copy the closest cookbook to
cookbooks/warehouse_safety/configs/pipeline.yamland adapt inputs, detector classes/SAM3 prompts, prompts, and question banks. - Dry-run the new cookbook, then run for real and validate outputs:
make run SCRIPT=workflow-runner:main \
ARGS='--cookbook-file cookbooks/warehouse_safety/configs/pipeline.yaml --container-dry-run'Guardrails
- Do not guess or fabricate the critical inputs enumerated in step 1; if any is missing or ambiguous, ask the user and confirm before executing.
- Do not customize a cookbook before a shipped example runs clean; a broken base makes domain debugging ambiguous.
- Keep the first custom pipeline minimal - only the stages needed for the requested annotations - and expand later.
- Verify that every selected stage's service package and image exist in the current branch before promising an end-to-end run.
- Do not put secrets, tokens, or absolute home paths in committed cookbooks; use
placeholders such as
<model-cache>and env vars for endpoint keys. - For reasoning-capable models (for example Gemini 3 Flash), raise
max_tokenson thevisual_qaandreasoningLLM substages to avoid the thinking-token tax; keep the default cap for non-reasoning models. - Do not rely on non-PAIDF pipelines, commands, or file locations. A first run
must be reproducible through
workflow-runner:maininside this repo.
© NVIDIA, 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
SKILL.md and 52 other files (references) in skills/paidf-auto-labeling of NVIDIA/skills.
- SKILL.md
- BENCHMARK.md
- evals/evals.json
- references/README.md
- references/cookbook-authoring.md
- references/cookbook-authoring/cookbook-schema.md
- references/cookbook-authoring/scenario-authoring.md
- references/event-and-person-attribute-search.md
- references/event-and-person-attribute-search/run-reference.md
- references/event-verification-reasoning.md
- references/event-verification-reasoning/run-reference.md
- references/pipeline-migration.md
- references/pipeline-migration/migration-playbook.md
- references/pipeline-migration/nonlinear-video-pipeline-example.md
- references/pipeline-migration/service-reuse-matrix.md
- … and 38 more
Open the folder on GitHubat commit dfdd080
Compare with similar skills
Paidf Auto Labeling 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 |
|---|---|---|---|---|---|---|
| Paidf Auto Labeling this skillNVIDIA/skills | 3.5k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| StartDonchitos/Claude-Code-Game-Studios | 26k | — | ~6.5k | Automated safety check: Pass | MIT | |
| Form Labelsthedaviddias/Front-End-Checklist | 74k | — | ~565 | Automated safety check: Pass | MIT | |
| Agentic Labelerdotnet/maui | 23k | — | ~3.5k | Automated safety check: Pass | MIT | |
| Skills CLI PR Labelingvercel-labs/skills | 33k | — | ~310 | Automated safety check: Pass | MIT | |
| Verdaccio PR Labelsverdaccio/verdaccio | 18k | — | ~1.6k | Automated safety check: Pass | MIT |
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Questions about Paidf Auto Labeling
What does Paidf Auto Labeling do?
A skill your agent uses when a user needs to get started with PAIDF Auto-Labeling, plan a scenario, run or debug a shipped cookbook, author prompts or cookbooks, migrate a pipeline, or configure a…. Paidf Auto Labeling is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when a user needs to get started with PAIDF Auto-Labeling, plan a scenario, run or debug a shipped cookbook, author prompts or cookbooks, migrate a pipeline, or configure a stage.
When should I use Paidf Auto Labeling?
Paidf Auto Labeling fits situations like: A user needs to get started with PAIDF Auto-Labeling; plan a scenario; debug a shipped cookbook; migrate a pipeline.
How do I install Paidf Auto Labeling in Claude Code?
Run `npx skills add NVIDIA/skills --skill paidf-auto-labeling -a claude-code`. Or copy the skill folder (skills/paidf-auto-labeling in NVIDIA/skills) into .claude/skills/paidf-auto-labeling in your project. Claude Code loads it when a task matches its description.
How do I install Paidf Auto Labeling in Codex?
Run `npx skills add NVIDIA/skills --skill paidf-auto-labeling -a codex`. Or copy the skill folder (skills/paidf-auto-labeling in NVIDIA/skills) into .agents/skills/paidf-auto-labeling in your project. Codex loads it when a task matches its description.
Can I use Paidf Auto Labeling 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/skills --skill paidf-auto-labeling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/paidf-auto-labeling, .gemini/skills/paidf-auto-labeling, .github/skills/paidf-auto-labeling and .opencode/skills/paidf-auto-labeling in your project.
What does Paidf Auto Labeling need to run?
Going by SKILL.md and its folder, Paidf Auto Labeling needs the command-line tools its instructions call (make).
Does Paidf Auto Labeling 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 Paidf Auto Labeling 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 Paidf Auto Labeling use?
Paidf Auto Labeling 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 Paidf Auto Labeling use?
About 1.8k tokens (SKILL.md is roughly 7.3k 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 45k tokens, read only when the agent opens those files.
What are the alternatives to Paidf Auto Labeling?
Skills that share tags, products or a category with Paidf Auto Labeling: Start (Donchitos/Claude-Code-Game-Studios, 26k stars), Form Labels (thedaviddias/Front-End-Checklist, 74k stars), Agentic Labeler (dotnet/maui, 23k stars) and Skills CLI PR Labeling (vercel-labs/skills, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Paidf Auto Labeling?
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,546 GitHub stars. The repository holds 386 skills in this directory. The repository was last updated on October 9, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.