python-polars
Enforces Polars over Pandas for functional pipe-style data manipulation (like dplyr in R). Use when writing Python data processing code, data transformation pipelines, ETL workflows, or analytical queries—e.g., "process this CSV", "aggregate sales data", "filter and transform DataFrame", "group by and calculate metrics".
90
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Discovery
92%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 strong skill description that clearly articulates its purpose (enforcing Polars for data manipulation), provides excellent trigger terms with natural user phrases, and explicitly states when to use it. The main weakness is potential overlap with other data processing skills, though the Polars-specific framing provides some distinction.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'functional pipe-style data manipulation', 'data processing code', 'data transformation pipelines', 'ETL workflows', 'analytical queries', with concrete examples like 'aggregate sales data', 'filter and transform DataFrame', 'group by and calculate metrics'. | 3 / 3 |
Completeness | Clearly answers both what ('Enforces Polars over Pandas for functional pipe-style data manipulation') and when ('Use when writing Python data processing code, data transformation pipelines, ETL workflows, or analytical queries') with explicit trigger examples. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'process this CSV', 'aggregate sales data', 'filter and transform DataFrame', 'group by and calculate metrics', 'ETL workflows', 'data processing'. These are phrases users naturally use when requesting data work. | 3 / 3 |
Distinctiveness Conflict Risk | While it specifies Polars over Pandas and mentions dplyr-style, terms like 'data processing', 'CSV', and 'DataFrame' could overlap with general Python data skills. The Polars-specific focus helps but isn't strongly distinguished from other data manipulation skills. | 2 / 3 |
Total | 11 / 12 Passed |
Implementation
85%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 skill with excellent actionability through concrete code examples and comprehensive reference tables. The progressive disclosure is well-handled with clear pointers to supplementary files. The main weakness is some verbosity in the persuasive framing (Authority/Commitment/Social Proof sections) that assumes Claude needs convincing rather than just instructions.
Suggestions
Remove or condense the 'Authority/Commitment/Social Proof' subsections - Claude doesn't need persuasion, just clear directives
Trim the repeated 'pandas is bad/legacy/technical debt' commentary throughout - stating the mandate once is sufficient
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill contains some unnecessary verbosity like the 'Authority/Commitment/Social Proof' framing and repeated emphasis on 'pandas is bad'. The comparison tables are useful but the philosophical explanations could be trimmed since Claude understands functional programming concepts. | 2 / 3 |
Actionability | Provides fully executable code examples with clear pipe-style patterns, a comprehensive quick reference table mapping pandas to Polars operations, and concrete expression patterns that are copy-paste ready. | 3 / 3 |
Workflow Clarity | For a skill focused on coding style/library preference rather than multi-step processes, the workflow is clear: use Polars, write pipe-style chains, query Context7 for APIs. The exception protocol for pandas fallback provides a clear decision tree. | 3 / 3 |
Progressive Disclosure | Well-structured with clear sections, and appropriately references four separate files for advanced topics (migration patterns, anti-patterns, pipe-style guide, expression patterns) with clear guidance on when to load each. | 3 / 3 |
Total | 11 / 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.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Reviewed
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