databricks-migration-deep-dive
tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill databricks-migration-deep-diveExecute comprehensive platform migrations to Databricks from legacy systems. Use when migrating from on-premises Hadoop, other cloud platforms, or legacy data warehouses to Databricks. Trigger with phrases like "migrate to databricks", "hadoop migration", "snowflake to databricks", "legacy migration", "data warehouse migration".
Validation
69%| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (591 lines); consider splitting into references/ and linking | Warning |
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 11 / 16 Passed | |
Implementation
65%This is a comprehensive migration skill with strong actionability through executable Python code covering the full migration lifecycle. However, it suffers from being overly long for a single SKILL.md file, and the workflow lacks explicit validation gates that would prevent proceeding when migrations fail. The content would benefit from splitting detailed implementations into separate files and adding explicit checkpoint instructions.
Suggestions
Add explicit validation checkpoints between steps (e.g., '**STOP if validation fails** - do not proceed to Step 3 until Step 2 validation passes')
Split the detailed Python implementations into separate files (e.g., `scripts/migration_assessment.py`, `scripts/schema_migration.py`) and reference them from SKILL.md
Remove verbose dataclass docstrings and inline comments that explain obvious concepts (e.g., 'Information about source table for migration planning')
Add a quick-start section at the top showing the minimal commands to run a basic migration before diving into detailed code
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is reasonably efficient with good code examples, but includes some unnecessary elements like the verbose dataclass definitions, overly detailed comments, and explanatory text that Claude would already understand (e.g., explaining what migration waves are). | 2 / 3 |
Actionability | Provides fully executable Python code with concrete implementations for assessment, schema migration, data migration, pipeline conversion, and cutover planning. Code is copy-paste ready with clear function signatures and return types. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered (1-5) with logical progression, but validation checkpoints are embedded within code rather than explicitly called out as mandatory gates. The validate_migration function exists but there's no explicit 'stop if validation fails' workflow instruction. | 2 / 3 |
Progressive Disclosure | Content is organized into clear sections with a helpful overview table, but the skill is monolithic (~400 lines) with all code inline. The Resources section links to external docs, but detailed code could be split into separate reference files for each migration step. | 2 / 3 |
Total | 9 / 12 Passed |
Activation
90%This is a well-structured skill description with excellent trigger term coverage and clear 'Use when' guidance. The main weakness is the lack of specific concrete actions - 'comprehensive platform migrations' is somewhat vague about what actual tasks are performed (schema conversion, pipeline migration, data transfer, etc.).
Suggestions
Replace 'Execute comprehensive platform migrations' with specific actions like 'Convert ETL pipelines, migrate schemas, transform data models, and transfer datasets to Databricks'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (platform migrations to Databricks) and mentions source systems (Hadoop, cloud platforms, legacy data warehouses), but lacks specific concrete actions like 'convert pipelines', 'migrate schemas', or 'transform ETL jobs'. | 2 / 3 |
Completeness | Clearly answers both what (execute comprehensive platform migrations to Databricks from legacy systems) and when (explicit 'Use when' clause with specific scenarios plus 'Trigger with phrases' providing additional guidance). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms users would say: 'migrate to databricks', 'hadoop migration', 'snowflake to databricks', 'legacy migration', 'data warehouse migration' - these are realistic phrases users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Very clear niche focused specifically on Databricks migrations with distinct triggers mentioning specific platforms (Hadoop, Snowflake) - unlikely to conflict with general data processing or other migration skills. | 3 / 3 |
Total | 11 / 12 Passed |
Reviewed
Table of Contents
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