Content
76%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 tight, code-centric skill body with strong token efficiency and concrete wrong/correct patterns. Its main weakness is the absence of an explicit checkpointed workflow, which suits its reference nature but leaves workflow_clarity mid-range.
Suggestions
Add a short numbered 'How to apply grouping' workflow (configure features -> detect cell mode -> render -> verify) with a validation checkpoint to raise workflow_clarity above 3.
Make the countLeafRows example fully runnable by defining groupRow (e.g. table.getRowModel().rows[0]) so the snippet is copy-paste ready.
Consider moving the longer Common Mistakes wrong/correct pairs into a references/ file and linking from the body to improve progressive_disclosure now that the body approaches 80 lines.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean, code-first body that assumes Claude's competence — minimal prose, no library primer, every line (Setup, Core Patterns, Common Mistakes, API Discovery) earns its place. | 5 / 5 |
Actionability | Provides executable setup imports, cell-mode detection, and wrong/correct pairs; the recursive countLeafRows example references a groupRow that is not fully wired into a runnable context, a minor gap keeping it below 5. | 4 / 5 |
Workflow Clarity | Sections imply a loose flow (Setup -> Core Patterns -> Common Mistakes -> API Discovery) but there is no sequenced multi-step workflow with validation checkpoints; this is a patterns/mistakes reference rather than a checkpointed process. | 3 / 5 |
Progressive Disclosure | Well-organized sections with clearly signaled one-level-deep references (Load the separate 'aggregation' skill, source paths); no bundle files exist to split, and structure is good but the body is somewhat long for a skill with no external file references, so below 5. | 4 / 5 |
Total | 16 / 20 Passed |