spark-job-creator
Spark Job Creator - Auto-activating skill for Data Pipelines. Triggers on: spark job creator, spark job creator Part of the Data Pipelines skill category.
33
3%
Does it follow best practices?
Impact
82%
0.98xAverage 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/spark-job-creator/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 essentially a template placeholder with no substantive content. It repeats the skill name as its own trigger term and provides zero information about what concrete actions the skill performs or when it should be selected. It would be nearly impossible for Claude to make an informed selection decision based on this description.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Creates Apache Spark job configurations, generates PySpark scripts, defines data transformations and ETL workflows, configures Spark submit parameters.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about creating Spark jobs, writing PySpark code, building ETL pipelines, configuring Spark submit, or processing large-scale data with Apache Spark.'
Remove the duplicate trigger term ('spark job creator' is listed twice) and expand with varied natural language terms users might actually say, such as 'PySpark', 'Spark SQL', 'data pipeline', 'batch processing', 'ETL'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names 'Spark Job Creator' and 'Data Pipelines' but provides no concrete actions. There is no mention of what the skill actually does—no specific capabilities like creating Spark configurations, writing PySpark scripts, defining transformations, etc. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name itself, and the 'when' clause is just a repetition of the skill name rather than meaningful trigger guidance. Both dimensions are very weak. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'spark job creator' repeated twice. It misses natural user terms like 'PySpark', 'Spark submit', 'ETL pipeline', 'data transformation', 'Spark SQL', '.py', 'batch job', or 'data processing'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Spark' and 'Data Pipelines' provides some domain specificity that distinguishes it from generic coding or document skills, but the lack of concrete detail means it could overlap with other data engineering or pipeline-related skills. | 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 is an empty shell with no substantive content. It consists entirely of generic boilerplate that describes what a Spark job creator skill would do without providing any actual instructions, code examples, configurations, or workflows. It adds zero value beyond what Claude already knows about Spark.
Suggestions
Add concrete PySpark code examples showing how to create a Spark job (e.g., SparkSession initialization, reading data, transformations, writing output) with copy-paste ready code.
Define a clear multi-step workflow for creating a Spark job: project setup → SparkSession config → data source connection → transformations → output → validation/testing, with explicit validation checkpoints.
Include specific configuration examples (spark-submit commands, cluster configs, common spark.conf settings) and link to separate reference files for advanced topics like streaming, optimization, or deployment patterns.
Remove all generic boilerplate sections (Purpose, When to Use, Example Triggers) and replace with actionable content that teaches Spark job creation patterns Claude wouldn't already know.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, repeats 'spark job creator' excessively, and provides zero domain-specific information. Every section is padded with generic descriptions. | 1 / 3 |
Actionability | There is no concrete code, no executable commands, no specific Spark configurations, no PySpark examples, and no actual guidance on creating Spark jobs. The content only describes what the skill claims to do without showing how. | 1 / 3 |
Workflow Clarity | No workflow steps are defined at all. There is no sequence of actions, no validation checkpoints, and no process for creating a Spark job. The skill merely states it 'provides step-by-step guidance' without actually providing any. | 1 / 3 |
Progressive Disclosure | The content is a flat, monolithic block of generic text with no references to detailed materials, no links to examples or configuration templates, and no structured navigation to deeper content. | 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
4dee593
- Reviewed
Table of Contents
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