pandas-pro
Performs pandas DataFrame operations for data analysis, manipulation, and transformation. Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation tasks such as joining DataFrames on multiple keys, pivoting tables, resampling time series, handling NaN values with interpolation or forward-fill, groupby aggregations, type conversion, or performance optimization of large datasets.
86
92%
Does it follow best practices?
Impact
78%
1.11xAverage score across 6 eval scenarios
Passed
No known issues
Quality
Discovery
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 a strong skill description that excels across all dimensions. It provides specific concrete actions, comprehensive trigger terms that users would naturally use, explicit 'Use when' and 'Invoke for' clauses, and a clear niche focused on pandas DataFrame operations that distinguishes it from other data-related skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: joining DataFrames on multiple keys, pivoting tables, resampling time series, handling NaN values with interpolation or forward-fill, groupby aggregations, type conversion, and performance optimization of large datasets. | 3 / 3 |
Completeness | Clearly answers both 'what' (performs pandas DataFrame operations for data analysis, manipulation, and transformation) and 'when' (explicit 'Use when' clause plus an 'Invoke for' clause listing detailed trigger scenarios). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'pandas', 'DataFrame', 'data cleaning', 'aggregation', 'merging', 'time series', 'joining', 'pivoting', 'NaN values', 'forward-fill', 'groupby', 'type conversion'. These are terms a user working with pandas would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Clearly scoped to pandas DataFrame operations specifically, with distinct triggers like 'DataFrame', 'pandas', 'groupby', 'forward-fill', 'resampling time series' that are unlikely to conflict with general data or spreadsheet skills. | 3 / 3 |
Total | 12 / 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, well-structured pandas skill with excellent actionable code patterns, clear workflow sequencing with validation checkpoints, and good progressive disclosure via the reference table. The main weakness is minor verbosity — the intro line, some redundancy between code patterns and the constraints lists, and the Output Templates section could be tightened. Overall it serves as an effective and practical guide for pandas operations.
Suggestions
Remove the introductory sentence ('Expert pandas developer...') and the Output Templates section, as the code patterns and workflow already demonstrate the expected output format.
Consolidate the MUST DO/MUST NOT DO lists by removing items already demonstrated in the code patterns (e.g., vectorized operations, .copy() usage, chained indexing) to reduce redundancy.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient but includes some unnecessary framing (e.g., 'Expert pandas developer specializing in...' intro, the Output Templates section restates what's already shown). The MUST DO/MUST NOT DO lists partially repeat guidance already demonstrated in the code patterns. However, the code examples themselves are lean and well-chosen. | 2 / 3 |
Actionability | Excellent executable code examples throughout — vectorized operations, groupby, merge with validation, missing value handling, time series resampling, pivot tables, and memory optimization are all copy-paste ready with realistic patterns. The before/after comparisons add concrete guidance. | 3 / 3 |
Workflow Clarity | The 5-step core workflow is clearly sequenced with validation at step 4 including explicit assert statements. The merge pattern includes indicator-based validation for unmatched rows. The assess→design→implement→validate→optimize flow provides a clear feedback loop for data transformation tasks. | 3 / 3 |
Progressive Disclosure | The reference table cleanly signals five one-level-deep reference files with clear 'Load When' context, making it easy to navigate to detailed guidance. The main skill provides a solid overview with actionable patterns while deferring detailed topic coverage to separate files. | 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.
- Repository
- jeffallan/claude-skills
- Commit
3d95bb1
- Reviewed
Table of Contents
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