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neo4j-aura-graph-analytics-skill

Serverless Aura Graph Analytics (AGA) GDS Sessions — covers GdsSessions, AuraGraphDataScience, AuraAPICredentials, DbmsConnectionInfo, SessionMemory, get_or_create, remote graph projection with gds.v2.graph.project and gds.graph.project.remote, gds.v2 session endpoints, gds.v2.graph.construct, AuraDB Cypher API memory/sessionId projection, algorithms, write-back, and session lifecycle. Use for AuraDB-connected, self-managed Neo4j, or standalone DataFrame/Spark session workloads. Does NOT cover the embedded GDS plugin on Aura Pro or self-managed Neo4j — use neo4j-gds-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover Snowflake Graph Analytics — use neo4j-snowflake-graph-analytics-skill.

69

Quality

85%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

85%

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, well-structured skill that covers a complex topic (serverless GDS sessions) with clear executable examples across multiple deployment modes. The 8-step workflow with validation checkpoints, error table, and checklist make it highly actionable. Minor conciseness issues exist with some redundancy between the deployment table, when-to-use sections, and the final checklist repeating inline guidance, but overall token efficiency is reasonable for the breadth of coverage.

DimensionReasoningScore

Conciseness

The skill is mostly efficient with good code examples and tables, but includes some redundancy — the checklist at the end largely repeats guidance already given in the body, and some sections (like the deployment decision table) could be tighter. The 'When to Use' / 'When NOT to Use' sections overlap with the deployment table.

2 / 3

Actionability

Excellent actionability throughout — every step includes fully executable Python code or Cypher queries with concrete examples. Authentication, memory estimation, session creation (three modes), graph projection (three methods), algorithm execution, result retrieval, and cleanup all have copy-paste ready code.

3 / 3

Workflow Clarity

The 8-step workflow is clearly sequenced with explicit validation checkpoints (verify_session_connectivity, verify_db_connectivity, async job polling, write-before-delete warnings). Error recovery is addressed via the common errors table and the async polling pattern includes failure handling. The checklist provides a final verification pass.

3 / 3

Progressive Disclosure

The skill provides a comprehensive but navigable overview with clear references to deeper materials — references/workflows.md for full examples, references/limitations.md for feature tables and tiers, plus a WebFetch table for external docs. Content is appropriately split with the main file covering all key patterns and edge cases without becoming a monolithic wall.

3 / 3

Total

11

/

12

Passed

Description

85%

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 a strong, highly specific description that excels at completeness and distinctiveness by clearly defining its scope and explicitly referencing alternative skills for out-of-scope tasks. Its main weakness is that the trigger terms are heavily technical and jargon-laden, which could make it harder to match against more casual or natural user queries. The description is dense but informative, serving expert users well while potentially being less accessible for general discovery.

Suggestions

Add a few more natural-language trigger phrases alongside the technical terms, e.g., 'graph analytics sessions', 'run GDS algorithms on Aura', 'serverless graph data science' to improve matching for users who don't use exact API names.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions and concepts: GdsSessions, remote graph projection with specific API calls (gds.v2.graph.project, gds.graph.project.remote), gds.v2.graph.construct, AuraDB Cypher API memory/sessionId projection, algorithms, write-back, and session lifecycle management.

3 / 3

Completeness

Clearly answers both 'what' (GDS sessions, remote graph projection, algorithms, write-back, session lifecycle) and 'when' ('Use for AuraDB-connected, self-managed Neo4j, or standalone DataFrame/Spark session workloads'). It also explicitly states when NOT to use it with references to alternative skills, which strengthens the 'when' guidance.

3 / 3

Trigger Term Quality

Includes many technical terms like 'GdsSessions', 'AuraGraphDataScience', 'gds.v2.graph.project', 'AuraDB', 'remote graph projection' which are relevant for expert users. However, these are heavily jargon-laden and may miss more natural user phrasings like 'graph analytics in the cloud', 'run algorithms on Aura', or 'GDS session'. Coverage is deep but narrow in vocabulary.

2 / 3

Distinctiveness Conflict Risk

Exceptionally distinctive with explicit boundary-drawing: explicitly states it does NOT cover embedded GDS plugin (use neo4j-gds-skill), Cypher authoring (use neo4j-cypher-skill), or Snowflake Graph Analytics (use neo4j-snowflake-graph-analytics-skill). This clear delineation makes conflict with other skills very unlikely.

3 / 3

Total

11

/

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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