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
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 extremely weak, consisting essentially of just the skill name repeated as a trigger term and a category label. It provides no information about what the skill actually does, no concrete actions, and no guidance on when Claude should select it. It would be nearly indistinguishable from any other Spark-related skill in a multi-skill environment.
Suggestions
Add concrete actions the skill performs, e.g., 'Analyzes and optimizes Spark SQL queries, rewrites inefficient joins, suggests partition strategies, and reviews execution plans for performance bottlenecks.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about slow Spark queries, query optimization, execution plans, catalyst optimizer, shuffle operations, or Spark SQL performance tuning.'
Remove the duplicated trigger term and expand with varied natural keywords users would actually say, such as 'Spark performance', 'optimize query', 'slow job', 'explain plan', 'DataFrame optimization', '.sql files'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions whatsoever. It only names the skill ('Spark Sql Optimizer') and its category ('Data Pipelines') without describing what it actually does—no mention of optimizing queries, analyzing execution plans, rewriting SQL, or any other specific capability. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no explicit 'Use when...' clause—only a generic auto-activation statement with a duplicated trigger term. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'spark sql optimizer' repeated twice. There are no natural user keywords like 'query performance', 'slow query', 'execution plan', 'optimize', 'Spark SQL', 'DataFrame', or 'catalyst optimizer' that users would naturally say. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Spark SQL' provides some domain specificity that distinguishes it from generic data or SQL skills, but the lack of concrete actions and the vague 'Data Pipelines' category could cause overlap with other data engineering or SQL optimization 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 template with no substantive content. It contains only generic boilerplate text that repeats the skill name without providing any actual Spark SQL optimization techniques, code examples, configuration guidance, or actionable instructions. It fails on every dimension of the rubric.
Suggestions
Add concrete, executable Spark SQL optimization examples (e.g., partition pruning, broadcast joins, predicate pushdown) with before/after code snippets showing the optimization impact.
Include a clear workflow for diagnosing and optimizing slow Spark SQL queries, such as: check explain plan → identify bottlenecks → apply specific optimization → validate improvement.
Replace all generic boilerplate sections ('When to Use', 'Capabilities', 'Example Triggers') with actual technical content covering common Spark SQL anti-patterns and their fixes.
Add references to advanced topics like AQE (Adaptive Query Execution), cost-based optimization settings, and join strategy selection in separate linked files if needed.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, repeats 'spark sql optimizer' excessively, and provides zero actual technical content about Spark SQL optimization. | 1 / 3 |
Actionability | There are no concrete code examples, no specific Spark SQL optimization techniques, no commands, and no executable guidance whatsoever. Every section is vague and abstract. | 1 / 3 |
Workflow Clarity | No workflow steps are defined. Claims like 'provides step-by-step guidance' and 'validates outputs' are stated but never actually delivered in the content. | 1 / 3 |
Progressive Disclosure | The content is a flat, uninformative page with no references to detailed materials, no links to examples or advanced topics, and no meaningful structure beyond generic headings. | 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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