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spark-optimization

Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

66

Quality

78%

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tessl review fix ./skills/spark-optimization/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A highly actionable collection of executable Spark optimization patterns, weakened by generic boilerplate sections, some over-explanation of basic Spark concepts, no validation checkpoints for destructive writes, and no progressive disclosure via bundle files.

Suggestions

Remove the generic "Instructions" and "Do not use this skill when" boilerplate and the "Core Concepts" execution-model diagram, which explain concepts Claude already knows.

Add explicit validation checkpoints (e.g., verify partition counts, check write success) around destructive `.write.mode("overwrite")` and batch operations to form a validate→fix→retry loop.

Split the seven patterns and configuration cheat sheet into reference files under references/ and link to them one level deep from SKILL.md to improve progressive disclosure.

DimensionReasoningScore

Conciseness

Mostly efficient code, but the generic "Instructions"/"Do not use this skill when" boilerplate and a "Core Concepts" section explaining Driver/Job/Stage/Task (concepts Claude already knows) add unnecessary padding.

3 / 5

Actionability

Seven patterns plus a Quick Start and config cheat sheet provide copy-paste-ready, executable code covering partitioning, joins, caching, memory, shuffle, formats, and monitoring.

5 / 5

Workflow Clarity

This is a pattern catalog rather than a sequenced workflow, and batch/destructive operations like `.write.mode("overwrite")` lack explicit validate→fix→retry checkpoints, capping the score at 3.

3 / 5

Progressive Disclosure

The ~420-line body inlines all patterns and the config cheat sheet with no bundle file references (only external URLs); section headers give some structure but content that could be split into separate files is not.

3 / 5

Total

14

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, specific description that names the domain and several concrete actions with an explicit Use-when trigger clause. Minor keyword synonym coverage gaps keep trigger quality just below the top anchor.

DimensionReasoningScore

Specificity

"Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning" lists multiple specific concrete actions with comprehensive coverage of the optimization domain.

5 / 5

Completeness

It explicitly answers both what ("Optimize Apache Spark jobs with...") and when ("Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines") with concrete trigger phrases.

5 / 5

Trigger Term Quality

"improving Spark performance, debugging slow jobs, or scaling data processing pipelines" provides good natural keyword coverage, but misses common synonyms or file/format extensions.

4 / 5

Distinctiveness Conflict Risk

"Apache Spark" is a clear niche with distinct triggers and minimal overlap risk with other skills.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
rmyndharis/antigravity-skills
Reviewed

Table of Contents

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