spark-sql-optimizer
Spark Sql Optimizer - Auto-activating skill for Data Pipelines. Triggers on: spark sql optimizer, spark sql optimizer Part of the Data Pipelines skill category.
34
Quality
3%
Does it follow best practices?
Impact
88%
1.02xAverage 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-sql-optimizer/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 placeholder with no substantive content. It only provides the skill name, a duplicate trigger term, and a generic category. Users and Claude cannot determine what this skill actually does or when to use it beyond exact phrase matching on 'spark sql optimizer'.
Suggestions
Add specific actions the skill performs, e.g., 'Analyzes Spark SQL queries for performance issues, suggests optimizations, rewrites inefficient joins, and explains Catalyst optimizer behavior.'
Add a proper 'Use when...' clause with natural trigger terms: 'Use when optimizing Spark queries, debugging slow SQL performance, understanding execution plans, or improving DataFrame operations.'
Include common user phrases and variations: 'spark query slow', 'optimize spark sql', 'catalyst optimizer', 'query execution plan', 'spark performance tuning'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions - only the name 'Spark Sql Optimizer' and category 'Data Pipelines'. There are no verbs describing what the skill actually does (e.g., optimize queries, analyze execution plans, rewrite SQL). | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and the 'when' clause is just a duplicate of the skill name rather than meaningful trigger guidance. Both components are essentially missing. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'spark sql optimizer' repeated twice. Missing natural variations users would say like 'optimize spark query', 'slow spark sql', 'query performance', 'execution plan', or 'catalyst optimizer'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Spark SQL' provides some specificity to a particular technology, but 'Data Pipelines' is generic and could overlap with other ETL or data processing skills. Without concrete actions, it's unclear what distinguishes this from other Spark-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 content is a generic template with no actual Spark SQL optimization knowledge. It contains only boilerplate descriptions of what a skill should do without any concrete techniques, code examples, or optimization strategies. The content fails to teach Claude anything it doesn't already know.
Suggestions
Add concrete Spark SQL optimization techniques with executable code examples (e.g., broadcast joins, partition pruning, predicate pushdown)
Include specific before/after query examples showing optimization patterns and their performance impact
Provide a workflow for analyzing and optimizing slow Spark SQL queries with validation steps (EXPLAIN plans, metrics to check)
Replace generic capability descriptions with actual optimization rules, anti-patterns to avoid, and configuration recommendations
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing specific about Spark SQL optimization. Phrases like 'provides automated assistance' and 'follows industry best practices' are filler that Claude already understands. | 1 / 3 |
Actionability | No concrete code, commands, or specific optimization techniques are provided. The content describes what the skill does abstractly but gives zero executable guidance on how to actually optimize Spark SQL queries. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. There are no validation checkpoints or sequences for performing Spark SQL optimization tasks. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of generic text with no references to detailed materials, examples, or related documentation. No structure for discovery or navigation. | 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
0c08951
- Reviewed
Table of Contents
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