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.
The content is highly actionable with executable examples across DABs, SDK, and CLI, and uses progressive disclosure effectively via real reference files. The main gaps are a slightly long inline template block and the absence of an explicit validation feedback loop in the development workflow.
Suggestions
Add an explicit feedback loop to the Development Workflow: after `bundle validate`, state 'If validation fails, fix errors and re-validate before deploying'.
Move the inline CLAUDE.md/AGENTS.md template into a reference file (e.g. references/project-scaffolding.md) and link to it, trimming the SKILL.md body.
Tighten the run_if and permission-level enumerations by linking to the relevant reference sections instead of fully inlining both the list and the examples.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and mostly assumes Claude's competence — it avoids explaining what Databricks or jobs are — but the inline CLAUDE.md/AGENTS.md template block and some enumerations (run_if, permission levels) could be trimmed slightly. | 4 / 5 |
Actionability | Provides copy-paste-ready code and commands across all three interfaces (DABs YAML, Python SDK, CLI) for quick start, common operations, compute, parameters, and permissions, covering the common cases. | 5 / 5 |
Workflow Clarity | The Development Workflow lists Validate → Deploy → Run → Check status with concrete commands, and validation is present, but there is no explicit 'only proceed when valid' feedback loop or error-recovery checkpoint for these batch/deploy operations. | 4 / 5 |
Progressive Disclosure | SKILL.md is a well-signaled overview with a reference table and two summary tables linking to four one-level-deep reference files; all referenced files exist and their section anchors resolve to real headings. | 5 / 5 |
Total | 18 / 20 Passed |