pyspark-transformer
Pyspark Transformer - Auto-activating skill for Data Pipelines. Triggers on: pyspark transformer, pyspark transformer Part of the Data Pipelines skill category.
36
Quality
3%
Does it follow best practices?
Impact
100%
1.04xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/11-data-pipelines/pyspark-transformer/SKILL.mdQuality
Discovery
7%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 description is severely underdeveloped, essentially serving as a label rather than a functional description. It provides no information about what capabilities the skill offers, what actions it can perform, or when Claude should use it. The repeated trigger term and boilerplate category mention suggest this may be auto-generated without meaningful content.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Creates PySpark DataFrame transformations, writes ETL pipelines, performs data aggregations and joins, handles schema operations'
Add a 'Use when...' clause with natural trigger terms like 'Use when the user needs help with PySpark code, Spark DataFrames, data transformations, ETL pipelines, or distributed data processing'
Include common variations of terminology users might use: 'spark', 'dataframe', 'ETL', 'data pipeline', 'transformation', '.py spark scripts'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names 'Pyspark Transformer' without describing any concrete actions. There are no verbs or specific capabilities listed - it doesn't explain what transformations, operations, or tasks this skill performs. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming itself, and has no 'Use when...' clause or equivalent guidance for when Claude should select this skill. Both what and when are essentially missing. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'pyspark transformer' repeated twice. Missing natural variations users would say like 'spark dataframe', 'data transformation', 'ETL', 'pipeline processing', or 'PySpark code'. | 1 / 3 |
Distinctiveness Conflict Risk | While 'Pyspark Transformer' is somewhat specific to PySpark technology, the lack of detail about what it actually does means it could conflict with other data processing or Python-related skills. The category mention 'Data Pipelines' provides some context but is insufficient. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill content is essentially a placeholder template with no substantive information. It describes what a skill should do in generic terms but provides zero actionable guidance, no code examples, no PySpark-specific patterns, and no actual instructions for implementing transformers. The entire content could be deleted without losing any useful information.
Suggestions
Add concrete, executable PySpark transformer code examples (e.g., creating a custom Transformer class extending pyspark.ml.Transformer)
Include specific patterns for common transformer use cases like feature engineering, data cleaning, or schema transformations
Provide a clear workflow for developing, testing, and deploying PySpark transformers with validation steps
Remove all generic boilerplate text and replace with PySpark-specific guidance that Claude wouldn't already know
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actionable information. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it does in abstract terms but never shows how to actually implement a PySpark transformer or provides any executable examples. | 1 / 3 |
Workflow Clarity | No workflow steps are defined. The content mentions 'step-by-step guidance' but provides none. There are no sequences, validation checkpoints, or any actual process to follow. | 1 / 3 |
Progressive Disclosure | The content is a flat, uninformative document with no references to detailed materials, no links to examples or API references, and no structure that would help navigate to actual implementation details. | 1 / 3 |
Total | 4 / 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 | |
- Commit
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- Reviewed
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