Content
75%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 well-structured, highly actionable overview with excellent reference routing and strong troubleshooting coverage. Its main weaknesses are redundancy (duplicated runtime/version details, repeated redirects, boilerplate Pydantic example) and a dangling examples/ folder reference.
Suggestions
Consolidate the runtime/pre-installed-package details (currently duplicated in Quick Reference and Platform Constraints, with pinned versions in both) into a single section, or move the pinned version list into a reference file to avoid time-sensitive drift.
Remove the generic Pydantic models example — Claude already knows Pydantic syntax — and keep only Databricks-specific patterns like the backend toggle and SQL warehouse connection.
Reduce the databricks-apps redirect to a single prominent pointer (e.g., the opening blockquote) instead of repeating it in the description, workflow, and Related Skills.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient and information-dense, but runtime/pre-installed details (Python 3.11, Ubuntu 22.04, 2 vCPU, 6 GB, pinned package versions) are duplicated in both Quick Reference and Platform Constraints, a generic Pydantic model example restates knowledge Claude already has, and the databricks-apps redirect appears four times. Could be meaningfully tightened. | 3 / 5 |
Actionability | Provides copy-paste-ready guidance throughout: exact app.yaml commands per framework, an executable SQL warehouse connection snippet, the backend toggle pattern, and precise troubleshooting commands (e.g., "databricks apps logs <name>", DATABRICKS_APP_PORT binding, st.set_page_config() ordering). Common cases are covered with details correctly deferred to real reference files. | 5 / 5 |
Workflow Clarity | Clear sequence (confirm skill → select framework → decide auth/resources/data strategy → deploy) with a copy-and-verify checklist and a substantial error-recovery table. Validation is present but partly delegated to the deployment reference ("post-deployment verification") rather than explicit inline checkpoints, so it falls short of the explicit feedback-loop anchor. | 4 / 5 |
Progressive Disclosure | Six well-signaled one-level-deep references, each with a description and discovery keywords, and all six exist as real substantive files. However, the body cites an examples/ folder (fm-minimal-chat.py, fm-parallel-calls.py, etc.) that is not present in the bundle, and some duplicated detail (runtime, pre-installed versions) belongs in references. | 4 / 5 |
Total | 16 / 20 Passed |