Content
92%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
An action-dense, well-structured skill body: executable examples across all three runtimes, explicit validation checkpoints and a recovery table for batch/destructive writes, and clean one-level-deep references to real bundle files. Only minor conciseness trimming of duplicated setup examples would move it higher.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and mostly token-efficient — tables and code carry the load and there is no padding about what Neo4j or Spark is — but version matrices and duplicated setup examples across PySpark/Scala/Databricks add some length that could be trimmed or delegated to references. | 4 / 5 |
Actionability | Copy-paste-ready executable code spans PySpark, Scala, and Databricks for setup, reads, writes, relationships, pre-write scripts, Delta pipelines, and tuning, with concrete option strings and exact Maven coordinates covering the common cases. | 5 / 5 |
Workflow Clarity | Sequences are explicit with validation checkpoints — the Delta pipeline 'nodes before relationships' ordering, the Overwrite-mode requirement to create uniqueness constraints before MERGE, the coalesce(1) guard for relationship writes, and a closing checklist with a feedback-oriented Common Errors table for recovery. | 5 / 5 |
Progressive Disclosure | SKILL.md is a well-signaled overview with two clearly named, one-level-deep references — [references/read-patterns.md] and [references/write-patterns.md], both real files — placing the bulk of the option reference off-page while keeping navigation explicit. | 5 / 5 |
Total | 19 / 20 Passed |