pandas-pro
tessl i github:jeffallan/claude-skills --skill pandas-proUse when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation, missing value handling, groupby operations, or performance optimization.
61%
Overall
Validation
Implementation
Activation
Validation
75%| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md line count is 97 (<= 500) | Pass |
frontmatter_valid | YAML frontmatter is valid | Pass |
name_field | 'name' field is valid: 'pandas-pro' | Pass |
description_field | 'description' field is valid (205 chars) | Pass |
description_voice | 'description' uses third person voice | Pass |
description_trigger_hint | Description includes an explicit trigger hint | Pass |
compatibility_field | 'compatibility' field not present (optional) | Pass |
allowed_tools_field | 'allowed-tools' field not present (optional) | Pass |
metadata_version | 'metadata' field is not a dictionary | Warning |
metadata_field | 'metadata' field not present (optional) | Pass |
license_field | 'license' field is missing | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
body_present | SKILL.md body is present | Pass |
body_examples | No examples detected (no code fences and no 'Example' wording) | Warning |
body_output_format | Output/return/format terms detected | Pass |
body_steps | Step-by-step structure detected (ordered list) | Pass |
Total | 12 / 16 Passed |
Implementation
42%This skill has good structural organization and progressive disclosure with clear references to detailed topic files. However, it critically lacks executable code examples, making it more of an abstract guideline document than an actionable skill. The MUST DO/MUST NOT DO lists are helpful constraints but would benefit from concrete code demonstrating the patterns.
Suggestions
- Add executable code examples for key operations (e.g., vectorized filtering, proper .loc[] usage, method chaining pattern) in a Quick Start section
- Replace abstract guidance like 'use vectorized operations instead of loops' with before/after code showing the anti-pattern and correct pattern
- Add specific validation commands to the workflow (e.g., `df.info()`, `df.isna().sum()`, `df.dtypes`) rather than just 'validate results'
- Remove or condense the 'Role Definition' section as it explains Claude's persona rather than providing actionable guidance
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some unnecessary content like the 'Role Definition' section that explains what Claude should already understand, and the 'Knowledge Reference' section is a list of keywords rather than actionable content. | 2 / 3 |
Actionability | The skill lacks any concrete, executable code examples. It provides abstract guidance like 'use vectorized operations' and 'use method chaining' without showing actual pandas code that Claude could copy and execute. | 1 / 3 |
Workflow Clarity | The 5-step core workflow provides a reasonable sequence, but lacks specific validation checkpoints or feedback loops. Steps like 'Validate results' are vague without concrete commands or checks to perform. | 2 / 3 |
Progressive Disclosure | The skill has a clear structure with a reference table pointing to topic-specific files (one level deep), well-organized sections, and appropriate separation of concerns between overview and detailed references. | 3 / 3 |
Total | 8 / 12 Passed |
Activation
72%This description has excellent trigger term coverage and distinctiveness for pandas-related work, making it easy for Claude to identify when to use it. However, it lacks an explicit 'what this does' statement and relies entirely on 'Use when/Invoke for' clauses, which makes it incomplete. The actions mentioned are category-level rather than concrete specific operations.
Suggestions
- Add an explicit capability statement before the trigger guidance, e.g., 'Performs data manipulation, transformation, and analysis using pandas DataFrames.'
- Make actions more concrete by specifying operations like 'reshape data with pivot tables, join datasets on keys, resample time series data, fill or drop missing values'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (pandas DataFrames) and lists several actions (data cleaning, aggregation, merging, time series analysis, missing value handling, groupby operations), but these are category-level terms rather than concrete specific actions like 'fill missing values with interpolation' or 'merge datasets on common keys'. | 2 / 3 |
Completeness | The description starts with 'Use when...' which addresses the 'when' question, but the 'what does this do' is only implied through the trigger contexts. There's no explicit statement of capabilities like 'Performs data manipulation and analysis using pandas' before the trigger guidance. | 2 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'pandas', 'DataFrames', 'data cleaning', 'aggregation', 'merging', 'time series', 'missing value', 'groupby', 'performance optimization'. These cover common variations of how users discuss data manipulation tasks. | 3 / 3 |
Distinctiveness Conflict Risk | Clearly targets pandas/DataFrame work with specific technical terms (groupby, time series, DataFrame) that distinguish it from general data skills or other programming skills. The pandas-specific terminology creates a clear niche. | 3 / 3 |
Total | 10 / 12 Passed |
Reviewed
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