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.

Quality

88%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

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, well-structured pandas skill with excellent actionability — nearly every pattern includes executable, production-ready code with validation. The workflow is clear with explicit validation checkpoints. The main weakness is that the body is somewhat long for an overview document, with substantial inline code that could be delegated to the referenced files, and the referenced files themselves are not provided in the bundle.

Suggestions

Move the detailed code patterns section into one of the referenced files (e.g., references/dataframe-operations.md) and keep only 1-2 key patterns inline to improve conciseness and progressive disclosure.

Remove the introductory tagline ('Expert pandas developer specializing in...') as it describes a persona rather than providing actionable guidance.

Dimension	Reasoning	Score
Conciseness	Generally efficient with good use of code examples, but includes some unnecessary framing ('Expert pandas developer specializing in...') and the output templates section is somewhat redundant given the constraints section. The before/after patterns are valuable and earn their tokens, but a few areas could be tightened.	2 / 3
Actionability	Excellent executable code examples throughout — vectorized operations, groupby aggregation, merge with validation, missing value handling, time series resampling, pivot tables, and memory optimization are all copy-paste ready with real pandas API calls. The before/after patterns are particularly actionable.	3 / 3
Workflow Clarity	The 5-step core workflow is clearly sequenced with explicit validation in step 4 (assert statements for shape, nulls, columns). The merge pattern includes indicator-based validation for unmatched rows. The assess→design→implement→validate→optimize flow provides a clear feedback loop.	3 / 3
Progressive Disclosure	The reference table with 5 topic-specific files is well-structured with clear 'Load When' guidance, but since no bundle files are provided, we cannot verify these references exist. The main SKILL.md includes substantial inline code patterns that could arguably be in reference files, making the body longer than necessary for an overview.	2 / 3
	Total	10 / 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 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 is clearly scoped to pandas DataFrame operations making it highly distinguishable 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' (pandas DataFrame operations for data analysis, manipulation, and transformation) and 'when' (explicit 'Use when' and 'Invoke for' clauses with 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', 'groupby', 'forward-fill', 'pivoting tables', and 'resampling time series' that are unlikely to conflict with general data or spreadsheet skills.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

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: e8be415

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

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