Check Cross Runtime
ayutaz/piper-plus
Python canonical (src/pythonrun/piperplus/, src/python/pipertrain/, src/python/g2p/piperplusg2p/) を変更した PR で、 ONNX I/O 以外の追随漏れ (phonemizer / config schema / CLI flag / data 形式 / API 変更) を 7 ランタイム +…
Agent skill
Physicalai Train Exporting And Validating
by open-edge-platform in open-edge-platform/physical-ai-studio
Exports and validates Physical AI Studio policies for Runtime deployment.
Install Physicalai Train Exporting And Validating
$ npx skills add open-edge-platform/physical-ai-studio --skill physicalai-train-exporting-and-validating -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install open-edge-platform/physical-ai-studio physicalai-train-exporting-and-validating --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-exporting-and-validating .claude/skills/physicalai-train-exporting-and-validating && 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-exporting-and-validating" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-exporting-and-validating into .claude/skills/physicalai-train-exporting-and-validating/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-exporting-and-validating", 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-exporting-and-validatingType 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-exporting-and-validating -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install open-edge-platform/physical-ai-studio physicalai-train-exporting-and-validating --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-exporting-and-validating .agents/skills/physicalai-train-exporting-and-validating && 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-exporting-and-validating" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-exporting-and-validating into .agents/skills/physicalai-train-exporting-and-validating/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-exporting-and-validating", 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-exporting-and-validating -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install open-edge-platform/physical-ai-studio physicalai-train-exporting-and-validating --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-exporting-and-validating .cursor/skills/physicalai-train-exporting-and-validating && 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-exporting-and-validating" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-exporting-and-validating into .cursor/skills/physicalai-train-exporting-and-validating/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-exporting-and-validating", 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-exporting-and-validating--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-exporting-and-validating -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install open-edge-platform/physical-ai-studio physicalai-train-exporting-and-validating --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-exporting-and-validating .gemini/skills/physicalai-train-exporting-and-validating && 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-exporting-and-validating" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-exporting-and-validating into .gemini/skills/physicalai-train-exporting-and-validating/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-exporting-and-validating", 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-exporting-and-validatingInstalls 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-exporting-and-validating -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-exporting-and-validating .github/skills/physicalai-train-exporting-and-validating && 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-exporting-and-validating" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-exporting-and-validating into .github/skills/physicalai-train-exporting-and-validating/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-exporting-and-validating", 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-exporting-and-validating -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-exporting-and-validating --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-exporting-and-validating .opencode/skills/physicalai-train-exporting-and-validating && 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-exporting-and-validating" agent skill from https://github.com/open-edge-platform/physical-ai-studio/tree/main/skills/library/physicalai-train-exporting-and-validating into .opencode/skills/physicalai-train-exporting-and-validating/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "physicalai-train-exporting-and-validating", 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-exporting-and-validating- GitHub stars
- 130
- Token cost
- ~1.1k tokens
- SKILL.md length
- 421 words
- Files
- 6 (incl. references)
- Skills in repo
- 7
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Exports and validates Physical AI Studio policies for Runtime deployment.
- Works in 6 steps: Identify the inputs: source policy class… → Pick the route and keep both consistent… → Read backend constraints before editing… → …
- Working on policy.export(...)
- SKILL.md covers Workflow, Validation loop, Backend notes and Required checks, plus 1 more section
- Calls uv
What it does
Physicalai Train Exporting And Validating is an agent skill from open-edge-platform/physical-ai-studio. Exports and validates Physical AI Studio policies for Runtime deployment. Use when working on policy.export(...), the physicalai export CLI, the ONNX/OpenVINO/Torch/ExecuTorch backends, export metadata, numerical parity checks, or the Studio side of the export/load contract that Runtime consumes with InferenceModel(...).
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/executorch.md`, `references/export-contract.md` and `references/onnx.md`).
It sits in DevOps & Cloud. It works with ONNX and 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
- Working on policy.export(...)
- The physicalai export CLI
- The ONNX/OpenVINO/Torch/ExecuTorch backends
- Export metadata
Example prompts
- “Use the physicalai-train-exporting-and-validating skill to export and validates Physical AI Studio policies for Runtime deployment”
- “/physicalai-train-exporting-and-validating”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Identify the inputs: source policy class (e.g. physicalai.policies.ACT), .ckpt path, target backend, and the Runtime loader behavior…
- Pick the route and keep both consistent — they must produce the same artifact
- Read backend constraints before editing generic code. See the backend reference for the target (references/.md). Do not generalize a fix…
- Export, then validate numerical parity against the Torch policy path on representative inputs. Parity proves correctness.
- Validate artifact structure and metadata against references/export-contract.md.
- Confirm the Runtime path. For deployment-bound artifacts, verify Runtime can auto-detect (by extension) or explicitly load the backend via…
What it can do on your machine
Read from SKILL.md and the folder at commit a80e54e. 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 Exporting And Validating loads about 1.1k tokens when it runs, and up to ~1.8k if it reads all its reference files. Until then it costs about 91 tokens; SKILL.md has 421 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 a80e54e, republished under its Apache-2.0 licence (© open-edge-platform). 421 words, ~1,057 tokens.
Download SKILL.mdSave it as
.claude/skills/physicalai-train-exporting-and-validating/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- physicalai-train-exporting-and-validating
- description
- Exports and validates Physical AI Studio policies for Runtime deployment. Use when working on policy.export(...), the physicalai export CLI, the ONNX/OpenVINO/Torch/ExecuTorch backends, export metadata, numerical parity checks, or the Studio side of the export/load contract that Runtime consumes with InferenceModel(...).
- license
- Apache-2.0
Exporting and Validating Studio Policies
Export lives in library/src/physicalai/export/: backends.py (the ExportBackend enum — onnx, openvino, torch, executorch — plus per-backend parameter classes) and mixin_policy.py (ExportablePolicyMixin, which gives policies export(output_dir, backend=...)). The Python API is primary library behavior; the CLI entry library/src/physicalai/cli/export.py must preserve the same artifact contract. Studio owns export; Runtime owns loading.
Workflow
- Identify the inputs: source policy class (e.g.
physicalai.policies.ACT),.ckptpath, target backend, and the Runtime loader behavior expected for that backend.- Done when: all four are pinned before touching code.
- Pick the route and keep both consistent — they must produce the same artifact:
- Python:
policy.export(output_dir, backend=ExportBackend.ONNX). - CLI:
physicalai export --policy physicalai.policies.ACT --ckpt_path model.ckpt --backend onnx --output_dir ./export.
- Python:
- Read backend constraints before editing generic code. See the backend reference for the target (
references/<backend>.md). Do not generalize a fix across backends without checking each. - Export, then validate numerical parity against the Torch policy path on representative inputs. Parity proves correctness.
- Done when: max abs/rel diff on sample inputs is within the family's tolerance, or the divergence is understood and documented.
- Validate artifact structure and metadata against
references/export-contract.md.- Done when: the expected model file and metadata files exist, and input/output/feature names match Runtime preprocessing.
- Confirm the Runtime path. For deployment-bound artifacts, verify Runtime can auto-detect (by extension) or explicitly load the backend via
InferenceModel(...).
Validation loop
Run export → validate → fix → repeat until both parity and structure pass:
# from library/
physicalai export --policy <ClassPath> --ckpt_path <model.ckpt> --backend <backend> --output_dir ./export
uv run --no-sync pytest tests/unit/export -k <backend>For API-facing changes, add or run an equivalent Python script/test that loads the checkpoint, calls policy.export("./export-api", backend=ExportBackend.<BACKEND>), and compares artifact metadata with the CLI output.
Treat parity (correctness) and latency/warmup (deployment viability) as separate checks; passing one does not imply the other.
Show full SKILL.md (146 more words)Show less
Backend notes
- onnx / openvino — deployment-oriented; Runtime core ships adapters, so artifacts load when deps are installed.
- torch — development/debugging; only claim deployment support when a matching Runtime adapter is installed and documented.
- executorch — optional, dependency-sensitive, edge/mobile; Runtime core ships no adapter in this package. Treat as available only with a documented companion distribution.
Required checks
- Export directory contains the expected backend model file and metadata files.
- Metadata names inputs/outputs/features consistently with Runtime preprocessing and action-chunk semantics.
- Python API export and CLI export produce equivalent artifact structure and metadata.
- Backend-specific dependencies are imported lazily or guarded with clear install guidance.
- Do not add a backend to user-facing docs unless Runtime can load it in-package or via a documented companion.
- CLI docs (
library/docs/how-to/export/) and Python API examples stay consistent.
References
references/export-contract.md— artifact requirements shared with Runtime (keep synchronized; CI should fail on divergence).references/onnx.md,references/openvino.md,references/torch.md,references/executorch.md— per-backend constraints.
© 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
SKILL.md and 5 other files (references) in skills/library/physicalai-train-exporting-and-validating of open-edge-platform/physical-ai-studio.
- SKILL.md
- references/executorch.md
- references/export-contract.md
- references/onnx.md
- references/openvino.md
- references/torch.md
Open the folder on GitHubat commit a80e54e
Compare with similar skills
Physicalai Train Exporting And Validating 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 Exporting And Validating this skillopen-edge-platform/physical-ai-studio | 130 | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| Check Cross Runtimeayutaz/piper-plus | 230 | — | ~3.5k | Automated safety check: Pass | MIT | |
| AWS Cdk Developmentzxkane/aws-skills | 367 | 2 repos | ~2.5k | Automated safety check: Pass | MIT | |
| Reproduce macOS Python FlavorsNuitka/Nuitka | 15k | — | ~1.7k | Automated safety check: Pass | AGPL-3.0 | |
| Env Var Conventionssgl-project/sglang | 37k | 2 repos | ~2.9k | Automated safety check: Pass | Apache-2.0 | |
| Pymobiledevice3 Device Operatordoronz88/pymobiledevice3 | 2.9k | — | ~1.8k | Automated safety check: Notes | GPL-3.0 |
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Related
Categories
Questions about Physicalai Train Exporting And Validating
What does Physicalai Train Exporting And Validating do?
Exports and validates Physical AI Studio policies for Runtime deployment. Physicalai Train Exporting And Validating is an agent skill from open-edge-platform/physical-ai-studio. Exports and validates Physical AI Studio policies for Runtime deployment.
When should I use Physicalai Train Exporting And Validating?
Physicalai Train Exporting And Validating fits situations like: working on policy.export(...); the physicalai export CLI; the ONNX/OpenVINO/Torch/ExecuTorch backends; export metadata.
How do I install Physicalai Train Exporting And Validating in Claude Code?
Run `npx skills add open-edge-platform/physical-ai-studio --skill physicalai-train-exporting-and-validating -a claude-code`. Or copy the skill folder (skills/library/physicalai-train-exporting-and-validating in open-edge-platform/physical-ai-studio) into .claude/skills/physicalai-train-exporting-and-validating in your project. Claude Code loads it when a task matches its description.
How do I install Physicalai Train Exporting And Validating in Codex?
Run `npx skills add open-edge-platform/physical-ai-studio --skill physicalai-train-exporting-and-validating -a codex`. Or copy the skill folder (skills/library/physicalai-train-exporting-and-validating in open-edge-platform/physical-ai-studio) into .agents/skills/physicalai-train-exporting-and-validating in your project. Codex loads it when a task matches its description.
Can I use Physicalai Train Exporting And Validating 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-exporting-and-validating -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-exporting-and-validating, .gemini/skills/physicalai-train-exporting-and-validating, .github/skills/physicalai-train-exporting-and-validating and .opencode/skills/physicalai-train-exporting-and-validating in your project.
What does Physicalai Train Exporting And Validating need to run?
Going by SKILL.md and its folder, Physicalai Train Exporting And Validating needs the command-line tools its instructions call (uv). Our summary lists: Python 3.
Does Physicalai Train Exporting And Validating 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 Exporting And Validating 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 Exporting And Validating use?
Physicalai Train Exporting And Validating 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 Exporting And Validating use?
About 1.1k tokens (SKILL.md is roughly 4.2k 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 730 tokens, read only when the agent opens those files.
What are the alternatives to Physicalai Train Exporting And Validating?
Skills that share tags, products or a category with Physicalai Train Exporting And Validating: Check Cross Runtime (ayutaz/piper-plus, 230 stars), AWS Cdk Development (zxkane/aws-skills, 367 stars), Reproduce macOS Python Flavors (Nuitka/Nuitka, 15k stars) and Env Var Conventions (sgl-project/sglang, 37k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Physicalai Train Exporting And Validating?
open-edge-platform (a GitHub organization) maintains it in open-edge-platform/physical-ai-studio, which has 130 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.