Content
86%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.
A well-structured, highly actionable reference skill that keeps the overview lean and delegates depth to six real, clearly signaled reference files. Minor conciseness and validation-loop gaps keep it just short of full marks.
Suggestions
Trim the "Common Pitfalls" recap or convert it to cross-references, since each item restates guidance already given in its section.
Add an explicit validate→fix→retry note for batch/NeighborLoader training (e.g., sanity-check that loss is computed only on seed nodes before proceeding).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is information-dense with executable code and tables and avoids explaining basics Claude already knows, but the "Common Pitfalls" section recapitulates points already made inline and the citation boilerplate adds tokens that could be trimmed. | 4 / 5 |
Actionability | Provides copy-paste-ready, executable code across the common cases (Data/datasets, GCN model, training+eval loops, NeighborLoader, HeteroData, to_hetero/HeteroConv, explainability) with a concrete conv-layer selection table. | 5 / 5 |
Workflow Clarity | Sections are clearly sequenced by topic and training loops show a train-then-eval checkpoint with a NeighborLoader seed-slicing correctness note, but there are no explicit validate→fix→retry feedback loops for batch training. | 4 / 5 |
Progressive Disclosure | SKILL.md acts as a well-signaled overview with six one-level-deep references ("Read references/X.md for ..."), all of which resolve to real files, with complete guides appropriately split out and quick patterns kept inline. | 5 / 5 |
Total | 18 / 20 Passed |