Borrowing it
Nothing to install: this file belongs to open-edge-platform/skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/open-edge-platform/skills/main/.agents/skills/physicalai-runtime-loading-exported-policies/SKILL.mdgit clone --depth 1 https://github.com/open-edge-platform/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/open-edge-platform/skills/physicalai-runtime-loading-exported-policies)<a href="https://agentmods.dev/skills/open-edge-platform/skills/physicalai-runtime-loading-exported-policies"><img src="https://agentmods.dev/badge/skills/open-edge-platform/skills/physicalai-runtime-loading-exported-policies/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/open-edge-platform/skills/physicalai-runtime-loading-exported-policies"><img src="https://agentmods.dev/badge/skills/open-edge-platform/skills/physicalai-runtime-loading-exported-policies.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00079 | $0.00717 |
| Opus 5 | $0.00039 | $0.00358 |
| Sonnet 5 | $0.00016 | $0.00143 |
| Haiku 4.5 | $0.00008 | $0.00072 |
Grade A, and why
physicalai-runtime-loading-exported-policies scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Loading Exported Policies
Runtime loads Studio export directories (or Hub snapshots that mirror them) through InferenceModel in src/physicalai/inference/model.py. Manifest parsing lives in src/physicalai/inference/manifest.py; backends register in src/physicalai/inference/adapters/registry.py (onnx → .onnx, openvino → .xml). Hub downloads use src/physicalai/inference/utils/_hub.py.
Workflow
-
Identify the artifact: local export directory or Hub
repo_id, expected backend, and whether the user needsselect_actionvspredict_action_chunk.- Done when: load path and API entry point are chosen before editing code.
-
Load with auto-detection first (local):
from physicalai.inference import InferenceModel model = InferenceModel("./exports/act_policy")- Done when:
modelconstructs without explicitbackend=when artifacts match a registered extension.
- Done when:
-
Hub load when the package is published:
model = InferenceModel.from_pretrained("OpenVINO/act-fp16-ov", revision="<commit-sha>")- Done when: revision is pinned for reproducibility when security or CI matters.
-
Explicit backend only when auto-detection is ambiguous:
model = InferenceModel("./exports/act_policy", backend="openvino", device="CPU") -
Validate structure against
references/export-load-contract.mdand backend notes (references/onnx.md,references/openvino.md).- Done when: manifest, model file, and processor artifacts resolve under the export directory.
-
Smoke inference without owning robot timing:
model.reset() action = model.select_action(observation)- Done when: one forward pass succeeds on representative observation keys/shapes. For hardware loops, hand off to
physicalai-runtime-running-policy-on-robot.
- Done when: one forward pass succeeds on representative observation keys/shapes. For hardware loops, hand off to
Validation loop
uv run pytest tests/unit/inference/test_model.py tests/unit/inference/test_manifest.py -q
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 69 lines · 79 tokens per session scan A 3e144a327cf8
physicalai-runtime-loading-exported-policies is a skill published in the GitHub repository open-edge-platform/skills (2 stars, last pushed today), licensed Apache-2.0. It adds 79 tokens to every session and 717 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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