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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.

70

Quality

86%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

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.

The content is a strong, highly actionable skill with clear sequencing, validation checkpoints, and good reference split. Its only notable gap is conciseness/structure: version-migration detail and some dense reference tables are inlined rather than pushed to a reference file.

DimensionReasoningScore

Conciseness

The body is dense with executable code and avoids explaining concepts Claude already knows (no 'what is a graph algorithm' padding), but inline version pins (1.22, <2) and a large in-body 2.0-alpha rename map add tokens that read more like reference material than overview.

4 / 5

Actionability

It provides copy-paste-ready executable code across auth, memory estimation, three session modes, four projection modes, mutate/stream/write algorithms, async polling, results retrieval, write-back, and cleanup — covering the common cases fully.

5 / 5

Workflow Clarity

Steps 1–8 are clearly sequenced with explicit validation checkpoints (verify_session_connectivity, verify_db_connectivity, poll job until RUNNING_DONE, 'Write before delete'), plus a Common Errors fix table and a Checklist — appropriate feedback loops for this billed/destructive-context skill.

5 / 5

Progressive Disclosure

It is a well-organized overview with two clearly signaled one-level-deep references (references/workflows.md, references/limitations.md) that both exist, but the sizable in-body 2.0 rename table and Cypher API blocks are reference-grade material that could be split out.

4 / 5

Total

18

/

20

Passed

Description

85%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.

The description is highly specific and complete, clearly delineating what the skill does and when to use it with strong boundary guidance against sibling skills. Its main weakness is trigger-term quality: it leans on API symbol names rather than the natural language a user would actually say.

Suggestions

Lead with a natural-language trigger clause (e.g., 'Run graph algorithms (PageRank, Louvain, FastRP) on Neo4j AuraDB or standalone DataFrames') before the API symbol list so users' phrasings match.

Trim or relocate the long enumeration of class/endpoint symbols (GdsSessions, AuraGraphDataScience, DbmsConnectionInfo, SessionMemory) — these aid disambiguation less than plain capability verbs.

Add common synonyms/file cues users say ('GDS on Aura', 'graph analytics serverless', 'AuraDB GDS session') to broaden natural keyword coverage.

DimensionReasoningScore

Specificity

Names the domain and comprehensively enumerates concrete capabilities (remote projection via gds.v2.graph.project/gds.graph.project.remote, gds.v2.graph.construct, Cypher API memory/sessionId projection, algorithms, write-back, session lifecycle), matching the 'multiple specific concrete actions; comprehensive coverage' anchor.

5 / 5

Completeness

It explicitly answers both what ('covers ... remote graph projection ... 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, matching the top anchor.

5 / 5

Trigger Term Quality

It includes relevant deployment terms ('AuraDB-connected', 'self-managed Neo4j', 'standalone DataFrame/Spark session workloads', 'write-back') but is dominated by internal API symbols (GdsSessions, AuraGraphDataScience, gds.v2.graph.project) and misses the most natural user phrasings like 'run graph algorithms on Aura' or 'GDS on AuraDB'.

3 / 5

Distinctiveness Conflict Risk

It carves a clear niche (serverless Aura GDS Sessions) and adds explicit disambiguation ('Does NOT cover ... use neo4j-gds-skill / neo4j-cypher-skill / neo4j-snowflake-graph-analytics-skill'), giving minimal conflict risk.

5 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
neo4j-contrib/neo4j-skills
Reviewed

Table of Contents

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