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

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

65%

Reviews 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, actionable overview with executable SQL and good decision routing, but it suffers from a verbose critical-rule bullet and references to five detailed files that are absent from the bundle.

Suggestions

Tighten the long PARTITIONED BY/CLUSTER BY critical-rule bullet into a short rule plus a compact comparison table, removing text that repeats the Quick Start comments.

Add the missing reference files (1-managed-iceberg-tables.md through 5-external-engine-interop.md) to a references/ directory so the signaled progressive-disclosure navigation actually resolves.

For destructive or batch DDL patterns (e.g. disabling deletion vectors before UniForm, switching format-version), add an explicit validate/verify step so the workflow includes a feedback checkpoint.

Dimension	Reasoning	Score
Conciseness	The body is information-dense with no basic-concept padding, but the PARTITIONED BY/CLUSTER BY critical-rule bullet is a wall of explanatory text and some content repeats between Critical Rules and Quick Start comments, so it could be tightened.	2 / 3
Actionability	Provides fully executable, copy-paste-ready SQL (CREATE TABLE USING ICEBERG with real TBLPROPERTIES values and ALTER TABLE SET TBLPROPERTIES for UniForm), matching the 'fully executable code; copy-paste ready' anchor.	3 / 3
Workflow Clarity	A clear 'When to Use' routing table and a read/write capability matrix give decision guidance, but there are no explicit validation checkpoints or feedback loops for the DDL operations, capping it at the 'sequence present but checkpoints missing' level.	2 / 3
Progressive Disclosure	The design is strong — a Reference Files table plus a 'When to Use' routing table signal one-level-deep references — but the five referenced files (1-managed-iceberg-tables.md, etc.) do not exist in the bundle, so the signaled navigation is broken.	2 / 3
	Total	9 / 12 Passed

Description

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.

The description is specific, complete, and distinct: it enumerates concrete Iceberg-on-Databricks capabilities and provides an explicit 'Use when' trigger clause covering the main scenarios. It is verbose but every clause carries capability or trigger information.

Dimension	Reasoning	Score
Specificity	Names many concrete capabilities ('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'), matching the 'lists multiple specific concrete actions' anchor.	3 / 3
Completeness	Explicitly answers both 'what' (the capability list) and 'when' via an explicit 'Use when creating Iceberg tables, enabling External Iceberg Reads... configuring external engines... integrating with Snowflake catalog' trigger clause.	3 / 3
Trigger Term Quality	Covers natural terms a user would say for this need ('Iceberg tables', 'Snowflake', 'PyIceberg', 'OSS Spark', 'External Iceberg Reads'), giving good coverage rather than jargon-only phrasing.	3 / 3
Distinctiveness Conflict Risk	Scoped to a clear niche (Apache Iceberg on Databricks) with distinct, specific triggers unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 12 missing, 4 suspicious	Warning

	Total	15 / 16 Passed

Repository: databricks-solutions/ai-dev-kit
Commit: 1c43c21

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.