Content
82%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is highly actionable with concrete spec overrides, commands, and parameter defaults, and it externalizes spec templates to real reference files. It is dense but efficient, with minor room to tighten verbose explanations and to surface validation steps as a more explicit loop.
Suggestions
Tighten the multi-fidelity AutoML resume explanation into a shorter rule with a checklist, reducing conciseness overhead.
Add an explicit numbered validate→fix→retry loop for the train/resume checkpoint flow so validation checkpoints are unmistakable rather than embedded in prose.
Consider moving the large Per-Action Dataset Requirements and Spec Param inference tables into a reference file to keep SKILL.md a leaner overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and model-specific, assuming domain knowledge of TAO/AutoML without padding; minor stretches (e.g., the multi-fidelity resume explanation) could be tightened, so it sits just below fully lean. | 4 / 5 |
Actionability | Provides copy-paste-ready spec_override blocks for train/evaluate/inference with S3 variables defined, a concrete entrypoint command ('python -m nvidia_tao_pytorch.cv.nvpanoptix3d.entrypoint.nvpanoptix3d <action> -e <spec>'), and concrete parameter defaults covering common cases. | 5 / 5 |
Workflow Clarity | The Train Action Policy lays out a decision sequence (read skill_info.yaml, resolve override, route through AutoML) with validation guidance for resume jobs and error-pattern recovery; it is not a fully explicit validate→fix→retry loop throughout, leaving minor gaps. | 4 / 5 |
Progressive Disclosure | References to references/skill_info.yaml and references/spec_template_<action>.yaml are real, one level deep, and clearly signaled, and the body is organized into topical sections; the dense inline tables could arguably be externalized, leaving minor organization gaps. | 4 / 5 |
Total | 17 / 20 Passed |