model-specs
Use when asked about a catalog model's own static characteristics — architecture, dataset/training compatibility, serving runtime requirements — e.g. "what was DreamZero trained on" or "does pi0.5 need a fixed camera count." Not for live status, scaling, or calling a deployed model (see the models skill for that).
ソース情報
- リポジトリ
- redhat-et/physical-ai-skills
- ソースの最終更新活動
- 2026年8月4日 22:21
- 検出された SKILL.md の言語
- 英語
- スター
- 0
- フォーク
- 0
インストール方法
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
ソースファイルを確認
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
ファイルエクスプローラー
4 ファイルSKILL.md を表示中
SKILL.md
ソースの指示 · 読み取り専用プレビュー- name
- model-specs
- description
- Use when asked about a catalog model's own static characteristics — architecture, dataset/training compatibility, serving runtime requirements — e.g. "what was DreamZero trained on" or "does pi0.5 need a fixed camera count." Not for live status, scaling, or calling a deployed model (see the models skill for that).
MODEL SPECS — this skill is knowledge-only: no cluster calls, no
scaling/inference actions. It's the reference for what a catalog model
*is*, as opposed to the models skill (what a deployed instance is
currently doing).
## Scripts
Run via the shell tool: `python3 "$SKILLS_ROOT/model-specs/scripts/get_model_reference.py" --model-name <name>`.
Each catalog model with a documented dataset/training profile has a file
under `references/<model_name>.md` (e.g. `references/pi05.md`,
`references/dreamzero.md`), keyed by the same catalog directory name used
elsewhere (platform/base/models/<model_name>/). A model with no file here
either has no fine-tuning recipe on this platform or hasn't been
documented yet — don't guess its specifics from general knowledge of the
base model.
pi0.5's reference is a Dimension/Priority checklist table matching the
datasets skill's own DATASET COMPATIBILITY CHECKLIST row for row — read
them side by side by row number when validating a candidate dataset.
DreamZero's is plain training-data provenance prose instead of a table,
since it's inference-only on this platform (no recipe to check a dataset
against).
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