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databricks-serverless-migration

Migrate Databricks workloads from classic compute to serverless compute. Use when migrating notebooks, jobs, pipelines, or Scala JARs (`spark_jar_task`) from classic clusters to serverless, checking if existing code is serverless-compatible, or writing new serverless-compatible code. Provides concrete fixes for the serverless Spark Connect architecture and guides the full migration. Not for classic DBR version upgrades or cluster configuration changes within classic compute.

66

Quality

81%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

Content

62%

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 thorough and actionable migration skill with excellent workflow clarity, concrete executable examples, and well-defined validation checkpoints. However, it suffers severely from lack of conciseness — the same information is repeated across multiple sections (decision tree, category tables, quick fixes, debug table), and massive sections like the Failure Reporting Protocol and detailed pattern catalogs should be delegated to the referenced files rather than inlined. The skill would be significantly more effective at roughly 1/3 its current length with better delegation to reference files.

Suggestions

Move the detailed Category A-H pattern tables, the complete Quick Fixes section, and the Debug error table to their respective reference files (compatibility-checks.md, code-patterns.md, configuration-guide.md) — keep only the top 5-10 most common patterns inline with links to the full catalogs.

Move the entire Failure Reporting Protocol (schema, redaction checklist, anonymization pass, output template, URL-encoding recipe) to references/failure-reporting.md and replace with a 5-line summary: when to offer, where the report goes, and a link to the reference.

Deduplicate pattern information — each pattern currently appears in up to 4 places (decision tree, category table, quick fix, debug table). Choose one canonical location and cross-reference from others.

DimensionReasoningScore

Conciseness

This skill is extremely verbose at ~800+ lines. While much of the content is domain-specific knowledge Claude wouldn't have, there is massive redundancy: the same patterns appear in the decision tree, the Category A-H tables, the Quick Fixes section, and the Debug error table. Many fixes are listed 3-4 times in different formats. The failure reporting protocol alone spans hundreds of lines with exhaustive regex patterns and multi-step procedures that could be in a reference file.

1 / 3

Actionability

The skill excels at actionability with fully executable Python code examples, specific before/after patterns, concrete error messages mapped to fixes, complete JSON schemas for environment specs and failure reports, and copy-paste ready commands. Every migration pattern has a specific, executable fix.

3 / 3

Workflow Clarity

The 4-step migration lifecycle (Ingest → Analyze → Test → Validate) is clearly sequenced with explicit validation checkpoints (A/B comparison, post-rewrite lint pass A1, deterministic post-serialization scrub). The two-branch strategy has a clear decision tree for what goes to production vs test. Stopping conditions are explicit. Feedback loops for error recovery are well-defined.

3 / 3

Progressive Disclosure

The skill references 11 separate reference files (compatibility-checks.md, streaming-migration.md, etc.) which is good structure, but the SKILL.md itself contains enormous amounts of detail that should be in those reference files — the full Category A-H tables, the complete Quick Fixes section, the entire Failure Reporting Protocol, and the Debug error table are all inline. The references section at the bottom suggests these files exist but the body duplicates much of their content. Without bundle files to verify, the references appear well-organized but the main file is overloaded.

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.

This is an excellent skill description that hits all the marks. It provides specific concrete actions, includes rich natural trigger terms that users would actually say, explicitly addresses both what the skill does and when to use it with a 'Use when...' clause, and carves out a clear niche with exclusion boundaries. The 'Not for' clause is a particularly strong addition that reduces ambiguity.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: migrate notebooks, jobs, pipelines, Scala JARs, check serverless compatibility, write serverless-compatible code, provide fixes for Spark Connect architecture, and guide full migration. Also specifies what it does NOT do.

3 / 3

Completeness

Clearly answers both 'what' (migrate workloads, check compatibility, write serverless-compatible code, provide fixes for Spark Connect) and 'when' (explicit 'Use when...' clause listing specific trigger scenarios). Also includes a 'Not for' exclusion clause which further clarifies scope.

3 / 3

Trigger Term Quality

Excellent coverage of natural terms users would say: 'Databricks', 'serverless', 'classic compute', 'notebooks', 'jobs', 'pipelines', 'Scala JARs', 'spark_jar_task', 'serverless-compatible', 'Spark Connect', 'migration'. These are all terms a user working on this task would naturally use.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive with a very specific niche: Databricks classic-to-serverless migration. The explicit exclusion of 'classic DBR version upgrades or cluster configuration changes within classic compute' further reduces conflict risk with related but different skills.

3 / 3

Total

12

/

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.

Validation9 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (848 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

9

/

11

Passed

Repository
databricks/databricks-agent-skills
Reviewed

Table of Contents

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