databricks-reference-architecture
Implement Databricks reference architecture with best-practice project layout. Use when designing new Databricks projects, reviewing architecture, or establishing standards for Databricks applications. Trigger with phrases like "databricks architecture", "databricks best practices", "databricks project structure", "how to organize databricks", "databricks layout".
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/databricks-pack/skills/databricks-reference-architecture/SKILL.mdQuality
Discovery
89%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a solid description with excellent trigger terms and completeness, clearly specifying both when and what. Its main weakness is that the 'what' could be more specific about the concrete actions or artifacts produced (e.g., folder structures, module templates, CI/CD configs). Overall it would perform well in skill selection among a large set of skills.
Suggestions
Add more specific concrete actions/deliverables, e.g., 'Generates folder structures, notebook organization, CI/CD pipeline configs, and cluster configuration templates' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | It names the domain (Databricks) and mentions 'reference architecture' and 'best-practice project layout', but doesn't list multiple concrete actions like specific deliverables (e.g., folder structures, notebook organization, CI/CD pipelines, cluster configs). 'Implement reference architecture' is somewhat vague. | 2 / 3 |
Completeness | Clearly answers both 'what' (implement Databricks reference architecture with best-practice project layout) and 'when' (designing new projects, reviewing architecture, establishing standards) with explicit trigger phrases. | 3 / 3 |
Trigger Term Quality | Explicitly lists natural trigger phrases like 'databricks architecture', 'databricks best practices', 'databricks project structure', 'how to organize databricks', and 'databricks layout'. These are terms users would naturally say. | 3 / 3 |
Distinctiveness Conflict Risk | Highly specific to Databricks architecture and project structure. The niche is narrow and well-defined with Databricks-specific triggers, making it unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, highly actionable reference architecture skill with excellent executable examples covering the full Databricks stack. Its main weaknesses are the monolithic structure that could benefit from progressive disclosure into separate files, and the absence of explicit validation/verification steps between workflow stages. The content is mostly efficient but could trim the architecture diagram and some descriptive text.
Suggestions
Add explicit validation checkpoints between steps, e.g., 'Verify catalog creation: SHOW SCHEMAS IN prod_catalog' before proceeding to Asset Bundle configuration, and 'databricks bundle validate' before deploying.
Split detailed YAML configurations and maintenance scripts into separate reference files (e.g., COMPUTE.md, MAINTENANCE.md) and reference them from the main skill to improve progressive disclosure.
Remove or simplify the ASCII architecture diagram—the project structure tree and code examples already convey the architecture more actionably.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some unnecessary verbosity—the ASCII architecture diagram, while visually appealing, adds tokens without much actionable value that the code examples don't already convey. The prerequisites section explains things Claude would know. However, most content is substantive and not padded with basic explanations. | 2 / 3 |
Actionability | Excellent actionability with fully executable SQL, YAML, and Python code throughout. Every step provides copy-paste ready configurations—Unity Catalog setup, Asset Bundle config, job definitions, medallion pipeline code, and maintenance scripts are all concrete and complete. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced (1-5), and the pipeline has implicit ordering (bronze→silver→gold). However, there are no explicit validation checkpoints between steps—no 'verify the catalog was created before proceeding' or 'validate the bundle config before deploying.' For infrastructure/architecture setup involving destructive or batch operations, the lack of feedback loops caps this at 2. | 2 / 3 |
Progressive Disclosure | The skill is a long monolithic document (~200+ lines of substantive content) that could benefit from splitting detailed configurations (e.g., full job YAML, maintenance scripts) into separate reference files. External resource links are provided at the end, but the inline content is heavy and could be better organized with references to supplementary files. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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