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name: model-sharing description: "Prepare, inspect, and safely smoke-test Asteroid publishable models and upload metadata." metadata: disco-role: operating disable-model-invocation: true license: MIT

Model sharing and publishing

Use this sub-skill when the user wants to save a publishable Asteroid model, register an old sample rate, or prepare metadata for Zenodo upload.

Typical triggers

• save_publishable(...)
• upload_publishable(...)
• asteroid-upload
• asteroid-register-sr
• Zenodo metadata, model cards, or publishable artifacts
• legacy checkpoints missing sample_rate

What to do first

1. Confirm whether the user wants only a local publishable artifact or an actual upload.
2. Check whether the checkpoint already contains:
   • model_args
   • state_dict
   • dataset
   • licenses
   • infos
3. Determine whether a token or sandbox upload is involved.

Standard workflow

• Read references/publishable-models.md for the local artifact contract.
• Read references/cli-reference.md for the public upload and sample-rate registration entrypoints.
• Read references/troubleshooting.md when the upload path is blocked by credentials or network dependencies.
• Use scripts/smoke_publishable.py for a safe local save/metadata smoke check.

Safe boundaries

• Local save_publishable(...) smoke checks are fine.
• Real Zenodo uploads are credential-bound and should stay gated.
• Do not treat network upload as a default verification step.
• asteroid-register-sr is for legacy checkpoints that were saved before sample_rate was present.

Useful inputs

• uploader name
• affiliation
• git username
• Zenodo token or sandbox token
• recipe name
• final metrics dict
• dataset/task/license metadata

Troubleshooting reminders

• The model dict must include the required keys before saving or uploading.
• upload_publishable(...) needs a token unless one is present in ACCESS_TOKEN.
• unit_test=True is useful for safe metadata inspection, not a real upload.

Inputs to inspect

• whether the user wants local artifact prep or a real publication step
• uploader name, affiliation, and git username
• whether a Zenodo token or sandbox token is available
• whether the checkpoint is a fresh model or a legacy checkpoint missing sample_rate

Smoke sequence

1. Build a tiny model.
2. Serialize it.
3. Attach dataset and license metadata.
4. Save a publishable artifact in a temporary directory.
5. Inspect the generated model.pth without publishing it.

What to avoid

• Do not make real publication the default path.
• Do not pretend that a missing token is a transient coding error.
• Do not claim the sample-rate registration helper is for modern checkpoints.
• Do not recommend network uploads when the user only needs a local smoke.

Metadata reminders

• Required publishable keys must already be present in the model dict.
• The license list should come from the dataset or training source.
• The final metrics dict should be concrete, even for a smoke path.
• Recipe name and training config are part of the published metadata story.

Good questions to ask when unclear

• Is this just a local publishable artifact or an actual upload?
• Do you have a token available?
• Is the checkpoint legacy or modern?
• What dataset/task name should appear in the metadata?