databricks-iceberg
Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External Iceberg Reads (uniform) on Delta tables (including Streaming Tables and Materialized Views via compatibility mode), configuring external engines to read Databricks tables via Unity Catalog IRC, integrating with Snowflake catalog to read Foreign Iceberg tables
95
93%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%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 an excellent skill description that thoroughly covers a well-defined technical niche. It lists specific capabilities, includes abundant natural trigger terms that practitioners would use, and provides an explicit 'Use when...' clause with multiple concrete scenarios. The only minor weakness is that the description is quite dense and could benefit from slightly better formatting, but the content quality is very high.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and technologies: creating Iceberg tables, enabling External Iceberg Reads on Delta tables, configuring external engines via Unity Catalog IRC, integrating with Snowflake catalog, credential vending, PyIceberg, OSS Spark access. | 3 / 3 |
Completeness | Clearly answers both 'what' (Apache Iceberg tables on Databricks with specific capabilities listed) and 'when' (explicit 'Use when...' clause with four distinct trigger scenarios covering table creation, External Iceberg Reads, external engine configuration, and Snowflake integration). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms a user would use: 'Iceberg tables', 'Databricks', 'Uniform', 'Delta tables', 'Streaming Tables', 'Materialized Views', 'compatibility mode', 'Unity Catalog', 'IRC', 'Snowflake', 'PyIceberg', 'OSS Spark', 'credential vending', 'Iceberg v3'. These are the exact terms practitioners would use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche combining Apache Iceberg + Databricks + specific interop scenarios. The combination of technologies (Unity Catalog IRC, External Iceberg Reads, Snowflake interop, PyIceberg) makes it very unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a high-quality skill that efficiently covers a complex topic with multiple integration patterns. It excels at conciseness, actionability, and progressive disclosure, providing executable SQL examples and clear navigation to detailed sub-files. The main weakness is the lack of explicit validation/verification steps after operations like table creation or UniForm enablement, which prevents workflow clarity from reaching the top score.
Suggestions
Add a brief verification step after the Quick Start examples (e.g., `DESCRIBE EXTENDED table_name` to confirm Iceberg format or UniForm status), which would strengthen workflow clarity.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is dense and efficient. It avoids explaining what Iceberg or Delta Lake are, assumes Claude understands Databricks concepts, and every section delivers actionable information. The critical rules section packs essential constraints tightly, and tables are used effectively to compress information. | 3 / 3 |
Actionability | The Quick Start section provides fully executable SQL examples covering multiple scenarios (no clustering, PARTITIONED BY, CLUSTER BY on v2 and v3, UniForm enablement). The critical rules give specific property names and exact TBLPROPERTIES settings. The common issues table pairs each problem with a concrete solution. | 3 / 3 |
Workflow Clarity | The skill clearly sequences decision-making via the 'When to Use' section and provides a capability matrix, but lacks explicit validation checkpoints. For example, after enabling UniForm there's no verification step, and the Quick Start doesn't include a 'verify it worked' step. The common issues section partially compensates but validation is implicit rather than explicit. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure with a clear overview in SKILL.md and well-signaled one-level-deep references to five detailed files. The reference table includes summaries and keywords for each file, and the 'When to Use' section provides task-based navigation to the right sub-file. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- databricks-solutions/ai-dev-kit
- Commit
02aac8c
- Reviewed
Table of Contents
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