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neo4j-spark-skill

Use when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.

92

1.49x
Quality

88%

Does it follow best practices?

Impact

100%

1.49x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

77%

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

This is a strong, highly actionable skill with excellent executable examples covering all major use cases (reads, writes, Databricks setup, Delta Lake pipelines). The workflow clarity is well-done with explicit sequencing, error tables, and a comprehensive checklist. The main weaknesses are moderate verbosity from duplicated PySpark/Scala examples and inline reference tables, plus broken references to bundle files that don't exist.

Suggestions

Provide the referenced bundle files (references/read-patterns.md and references/write-patterns.md) or remove the broken links

Consider moving the full configuration options table and version matrix into a reference file to reduce the main skill's length

DimensionReasoningScore

Conciseness

The skill is generally efficient and avoids explaining basic concepts, but includes some redundancy — both PySpark and Scala examples for setup/reads/writes that are near-identical, and the version matrix and full config table add bulk. The content could be tightened by consolidating language variants and trimming the config table to non-obvious options.

2 / 3

Actionability

Excellent actionability throughout — every section provides fully executable, copy-paste-ready code examples for PySpark and Scala. Specific Maven coordinates, exact option strings, concrete DataFrame examples with sample data, and precise Databricks setup steps make this immediately usable.

3 / 3

Workflow Clarity

Multi-step workflows are clearly sequenced with explicit validation checkpoints. The Delta Lake → Neo4j pipeline explicitly orders nodes before relationships, the checklist serves as a verification step, the common errors table provides error recovery guidance, and the relationship write deadlock prevention (coalesce(1)) is called out at every relevant point.

3 / 3

Progressive Disclosure

The skill references two bundle files (references/read-patterns.md and references/write-patterns.md) for detailed options, which is good progressive disclosure design. However, no bundle files were provided, so these references are broken. The main file itself is quite long (~300 lines) and could benefit from moving the version matrix, full config table, or language-variant examples into reference files.

2 / 3

Total

10

/

12

Passed

Description

100%

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 is an excellent skill description that hits all the marks. It opens with a clear 'Use when' trigger clause, enumerates a comprehensive list of specific capabilities, includes abundant natural trigger terms, and explicitly delineates boundaries with other related skills to prevent misselection. The negative boundary clauses are a particularly strong feature for disambiguation in a multi-skill environment.

DimensionReasoningScore

Specificity

Lists numerous specific concrete actions: SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines.

3 / 3

Completeness

Clearly answers both 'what' (covers SparkSession setup, DataFrame reads/writes, partition tuning, Databricks config, etc.) and 'when' (opens with 'Use when reading from or writing to Neo4j with Apache Spark or Databricks'). Also explicitly states what NOT to use it for with cross-references to other skills.

3 / 3

Trigger Term Quality

Excellent coverage of natural terms users would say: Neo4j, Apache Spark, Databricks, SparkSession, DataFrame, PySpark, Scala, Delta Lake, MERGE, SaveMode, neo4j-connector-apache-spark, Cypher, relationship scan. These are all terms a user working in this domain would naturally use.

3 / 3

Distinctiveness Conflict Risk

Extremely distinctive with a clear niche (Neo4j + Spark/Databricks connector). The explicit 'Does NOT handle' clauses with references to specific alternative skills (neo4j-cypher-skill, neo4j-driver-python-skill, neo4j-gds-skill) actively prevent conflicts and make disambiguation trivial.

3 / 3

Total

12

/

12

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
neo4j-contrib/neo4j-skills
Reviewed

Table of Contents

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