Content
92%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 body is a lean, high-signal design brief: concrete function and constant names, an unambiguous batched-inference workflow with error handling, and an explicit validation test. The only weakness is that implementation details like the suggestion schema's shape and queue-spending logic are assumed rather than shown, which is acceptable for this codebase-internal skill but leaves minor actionability gaps.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Every line is a directive with zero padding ('Auto should not call the model on every placement', 'Do not add a backend or new dependency'); no concept Claude already knows is explained, so every token earns its place. | 5 / 5 |
Actionability | Concrete identifiers appear throughout (snapshotCells(), AUTO_SUGGESTIONS_SCHEMA, autoSuggestionQueue, AUTO_REFRESH_EVERY, normalizeAutoAction, adaptAutoSuggestionToCell(action, x, z)) plus a testable validation recipe ('verify two Auto suggestions only call the stub once'), but minor gaps remain such as the schema's shape and how the queue is spent. | 4 / 5 |
Workflow Clarity | The intended model is a clear ordered sequence (capture snapshot, request batch, spend locally, refresh after N placements or on empty), with an explicit error branch ('If no API key exists, open the Generate modal with the Auto key message') and an explicit validation section with pass criteria. | 5 / 5 |
Progressive Disclosure | The skill is under 50 lines with no bundle files and no need for external references; its three well-labeled sections (intended model, guardrails, validation) satisfy the rubric's simple-skill condition for a top score. | 5 / 5 |
Total | 19 / 20 Passed |