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.

Quality

90%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

88%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 dense, highly actionable API skill with a well-sequenced validated workflow, error-recovery table, and checklist, backed by real one-level reference files. The main gaps are token weight in the body and a few sections that belong in references rather than the overview.

Suggestions

Move the 'graphdatascience 2.0 (alpha)' rename map and minimums into references/migration.md, keeping only a one-line pointer in SKILL.md, to tighten the overview and isolate time-sensitive pre-release detail.

Relocate the detailed Cypher API session-management examples (gds.session.getOrCreate / gds.session.list) to references/workflows.md, summarizing the memory/sessionId projection rule inline instead.

Trim the Deployment Decision Table rows that duplicate the 'When NOT to Use' list (embedded plugin rows) to reduce repetition and save tokens.

Dimension	Reasoning	Score
Conciseness	The body is largely lean API/code reference that Claude would not already know, but it runs ~425 lines and the large 'graphdatascience 2.0 (alpha)' rename map is heavy detail for a pre-release that could be trimmed or moved to a reference.	4 / 5
Actionability	Steps 1–8 provide copy-paste-ready, fully executable Python with imports and env vars, plus concrete Cypher blocks covering connected, self-managed, standalone, and Pandas cases — specific examples cover the common cases.	5 / 5
Workflow Clarity	A clearly sequenced 8-step workflow with explicit validation checkpoints (verify_session_connectivity, verify_db_connectivity, async job polling until RUNNING_DONE), a Common Errors table for recovery, and a closing Checklist — satisfying the destructive/batch feedback-loop requirement.	5 / 5
Progressive Disclosure	Good structure with one-level-deep, well-signaled references to real files (references/workflows.md, references/limitations.md) and a WebFetch table, but the SKILL.md body itself is long and inlines detail (full 2.0 rename map, Cypher API session management) that could be offloaded to references.	4 / 5
	Total	18 / 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, third-person description that concretely states capabilities, explicit use-conditions, and crisp boundaries against three neighboring skills. Its only weakness is a tilt toward internal API names over the natural phrases a user would actually say.

Dimension	Reasoning	Score
Specificity	Lists many concrete capabilities — 'remote graph projection with gds.v2.graph.project and gds.graph.project.remote', 'gds.v2.graph.construct', 'AuraDB Cypher API memory/sessionId projection, algorithms, write-back, and session lifecycle' — giving comprehensive, specific coverage rather than vague abstractions.	5 / 5
Completeness	Explicitly answers both 'what' ('covers GdsSessions ... algorithms, write-back, and session lifecycle') and 'when' ('Use for AuraDB-connected, self-managed Neo4j, or standalone DataFrame/Spark session workloads') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Good natural-term coverage ('AuraDB-connected, self-managed Neo4j, or standalone DataFrame/Spark session workloads', 'Snowflake Graph Analytics'), but the description is dominated by API symbols (gds.v2.graph.project, SessionMemory) that users would not naturally say, leaving a few natural synonyms missing.	4 / 5
Distinctiveness Conflict Risk	Clear niche with explicit boundary clauses — 'Does NOT cover the embedded GDS plugin ... use neo4j-gds-skill', 'Does NOT handle Cypher authoring', 'Does NOT cover Snowflake Graph Analytics' — minimizing conflict risk with sibling skills.	5 / 5
	Total	19 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: neo4j-contrib/neo4j-skills
Path: neo4j-aura-graph-analytics-skill/SKILL.md
Commit: a3627d2

Reviewed: 4 days ago

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