The body is mostly efficient executable code, but the introductory paragraph and 'When to Use This Skill' list restate the description, and some prose could be trimmed; not fully lean, so it sits below the score-3 anchor.

Extensive copy-paste-ready PySpark code across all seven patterns — SparkSession configs, broadcast/bucket/salt joins, StorageLevel caching, memory tuning, and monitoring helpers — matches the fully-executable score-3 anchor.

The content is organized as a pattern catalog with implicit read-transform-write ordering, but there is no explicit multi-step workflow with validation checkpoints or feedback loops, so it does not reach the score-3 anchor.

No bundle files exist and the ~410-line body keeps all seven detailed patterns and the config cheat sheet inline with no external references; sections are well-organized but content that should be split remains in one file.

Names the Spark domain and lists multiple concrete capabilities — 'partitioning, caching, shuffle optimization, and memory tuning' — matching the multiple-specific-actions anchor rather than the partial score-2 anchor.

Explicitly answers both what ('Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning') and when via a 'Use when...' clause with multiple triggers, satisfying the score-3 anchor.

Natural user phrasings like 'improving Spark performance', 'debugging slow jobs', and 'scaling data processing pipelines' give good coverage of terms a user would actually say, not just technical jargon.

Apache Spark optimization is a clear niche with distinct triggers unlikely to overlap with unrelated skills; no first/second-person voice present to penalize.