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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

100

Quality

100%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

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, technologies, and integration points, and includes a comprehensive 'Use when...' clause with multiple concrete trigger scenarios. The description uses appropriate third-person voice and includes abundant natural trigger terms that practitioners would actually use.

DimensionReasoningScore

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 with very specific technical triggers like 'External Iceberg Reads', 'Unity Catalog IRC', 'credential vending', and 'Snowflake interop'. Extremely unlikely to conflict with other skills.

3 / 3

Total

12

/

12

Passed

Implementation

100%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is an exceptionally well-crafted skill file. It provides a concise but comprehensive overview of a complex topic (Iceberg on Databricks), with executable SQL examples, critical safety rules, a clear capability matrix, and excellent progressive disclosure to detailed reference files. The Common Issues table adds significant practical value by addressing real-world gotchas with specific solutions.

DimensionReasoningScore

Conciseness

The content is dense with domain-specific information that Claude wouldn't inherently know — Databricks-specific TBLPROPERTIES, version-specific behaviors, UniForm nuances, and critical gotchas. There's no padding or explanation of basic concepts. Every section earns its place.

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 Common Issues table gives specific solutions with exact property names and commands. The critical rules are concrete and specific.

3 / 3

Workflow Clarity

The 'When to Use' section provides clear decision routing for every major use case. The Critical Rules section establishes explicit constraints and validation checkpoints (e.g., must determine pattern before writing code, must disable DVs for CLUSTER BY on v2). The capability matrix clarifies read/write boundaries. For this type of skill (reference/configuration rather than multi-step destructive operations), the workflow guidance is appropriate and complete.

3 / 3

Progressive Disclosure

Excellent progressive disclosure — the SKILL.md serves as a clear overview with quick start examples, then routes to five well-organized reference files with descriptive summaries and keywords. References are one level deep, clearly signaled in both the Reference Files table and the When to Use section. Navigation is intuitive.

3 / 3

Total

12

/

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.

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository
databricks-solutions/ai-dev-kit
Reviewed

Table of Contents

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