Content
78%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, token-efficient skill body with excellent progressive disclosure and concrete data-model guidance. The main gap is the lack of executable DQL query examples inline, which slightly limits actionability.
Suggestions
Add one or two short executable DQL examples (a `fetch user.events` and a `timeseries dt.frontend.*` snippet) in the body to make guidance copy-paste ready.
Tighten the discursive frontend.name/Smartscape paragraph to its essential rule for marginal conciseness gains.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Dense, information-packed body with minimal padding that assumes Claude's competence; a few discursive passages (e.g., the frontend.name/Smartscape guidance) could be tightened. | 4 / 5 |
Actionability | Concrete field names, filter patterns, characteristic filters, a real policy statement, and quick-reference thresholds, but no copy-paste-ready DQL query examples in the body itself (those live in references). | 4 / 5 |
Workflow Clarity | The Drill-Down Pattern gives a clear numbered sequence and the Workflows table maps domains to references; no destructive/batch operations apply, but validation checkpoints are implicit rather than explicit feedback loops. | 4 / 5 |
Progressive Disclosure | Concise overview in SKILL.md with a clean Workflows table signaling one-level-deep references; all 11 referenced files exist and match the body, with no nested references and easy navigation. | 5 / 5 |
Total | 17 / 20 Passed |