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, actionable skill body with executable BAD/GOOD examples, a clear decision-tree workflow, and clean one-level-deep progressive disclosure. The only real lever is trimming repetition across the 'When NOT to use Effects' subsections.
Suggestions
Condense the five 'When NOT to Use Effects' subsections by collapsing repetitive BAD/GOOD scaffolding into a compact table or shared example, reducing token cost without losing the contrasts.
Add a one-line 'how to apply this during review' framing so the decision tree reads as a sequence with explicit checkpoints rather than a flat list.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean code-driven guidance with minimal padding, but five near-identical BAD/GOOD subsection blocks could be condensed further, keeping it just below the leanest anchor. | 4 / 5 |
Actionability | Every subsection provides complete, copy-paste-ready TSX/JSX with BAD and GOOD variants plus a concrete decision tree, fully covering the common cases. | 5 / 5 |
Workflow Clarity | The 7-step decision tree gives a clear sequenced selection workflow; not a destructive/batch task so validation loops aren't required, but no explicit checkpoints cap it just below 5. | 4 / 5 |
Progressive Disclosure | Overview body cleanly signals five one-level-deep references, each existing on disk with no further nesting — easy to navigate and appropriately split. | 5 / 5 |
Total | 18 / 20 Passed |