databricks-data-handling
Implement Delta Lake data management patterns including GDPR, PII handling, and data lifecycle. Use when implementing data retention, handling GDPR requests, or managing data lifecycle in Delta Lake. Trigger with phrases like "databricks GDPR", "databricks PII", "databricks data retention", "databricks data lifecycle", "delete user data".
85
83%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
89%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 a solid skill description with excellent trigger term coverage and clear 'when to use' guidance. Its main weakness is that the capability descriptions stay at a category level (GDPR, PII handling, data lifecycle) rather than listing specific concrete actions. The explicit trigger phrases and distinct Databricks/Delta Lake niche make it highly functional for skill selection.
Suggestions
Add more specific concrete actions to improve specificity, e.g., 'Execute GDPR deletion requests, anonymize PII columns, configure retention policies, implement vacuum schedules, and manage time travel for compliance audits.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Delta Lake data management) and some actions (GDPR, PII handling, data lifecycle), but the actions are described at a pattern/category level rather than listing specific concrete operations like 'anonymize PII columns', 'execute deletion requests', or 'set retention policies'. | 2 / 3 |
Completeness | Clearly answers both 'what' (implement Delta Lake data management patterns including GDPR, PII handling, and data lifecycle) and 'when' (explicit 'Use when' clause plus 'Trigger with phrases like' section providing concrete trigger guidance). | 3 / 3 |
Trigger Term Quality | Includes a good range of natural trigger terms: 'databricks GDPR', 'databricks PII', 'databricks data retention', 'databricks data lifecycle', 'delete user data'. These cover both platform-specific and action-oriented phrases users would naturally say. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche combining Delta Lake/Databricks with GDPR/PII/data lifecycle concerns. The specific platform (Databricks/Delta Lake) combined with the compliance domain makes it very unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, highly actionable skill with executable code for every step and a well-sequenced workflow covering the full GDPR/data lifecycle in Delta Lake. The dry-run pattern and audit logging demonstrate good safety practices. The main weakness is that the skill is quite long and could benefit from splitting detailed implementations into referenced files, and some minor verbosity could be trimmed.
Suggestions
Consider extracting the Python classes (GDPRHandler, RetentionEnforcer) and the SAR report function into separate referenced files (e.g., gdpr_handler.py, retention.py) to keep SKILL.md as a concise overview with quick-start examples.
Remove the Prerequisites bullet about 'Understanding of data classification requirements'—Claude already knows this context and it adds no actionable value.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with executable code and minimal fluff, but some sections could be tightened—e.g., the class docstrings, the Prerequisites section mentioning 'Understanding of data classification requirements' is unnecessary for Claude, and the Output section largely restates what the steps already cover. | 2 / 3 |
Actionability | Every step provides fully executable SQL and Python code that is copy-paste ready with concrete examples. The code includes class definitions, SQL DDL, masking functions, and usage patterns with specific table/column names. | 3 / 3 |
Workflow Clarity | The 6-step workflow is clearly sequenced from classification through deletion, retention, masking, security, and SAR reporting. Critical validation is built in via dry_run=True patterns with explicit instructions to always dry-run first, and the GDPR deletion includes audit logging as a feedback mechanism. | 3 / 3 |
Progressive Disclosure | The content is well-structured with clear sections and an error handling table, but it's quite long (~200+ lines of inline code) with no content split into separate reference files. The masking functions, retention enforcer, and SAR report could be referenced as separate files to keep the main skill leaner. The Resources section with external links is good, and the Next Steps reference is appropriate. | 2 / 3 |
Total | 10 / 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.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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