graph-database-expert

Expert in graph database design and development with deep knowledge of graph modeling, traversals, query optimization, and relationship patterns. Specializes in SurrealDB but applies generic graph database concepts. Use when designing graph schemas, optimizing graph queries, implementing complex relationships, or building graph-based applications.

Quality

83%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

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 well-structured, highly actionable skill with excellent executable code examples and clear TDD workflow. The main weakness is verbosity - it includes explanatory content about graph database concepts and landscape that Claude already knows, and could benefit from moving detailed patterns to reference files. The security and common mistakes sections are valuable but contribute to overall length.

Suggestions

Remove or significantly condense the 'When to Use/NOT Use Graph Databases' and 'Graph Database Landscape' sections - Claude knows these concepts

Move the '7 Graph Modeling Patterns' section to the referenced modeling-guide.md file and keep only a brief summary with links

Condense the Overview section to focus on SurrealDB-specific guidance rather than general graph database expertise

Dimension	Reasoning	Score
Conciseness	The skill is comprehensive but includes some unnecessary explanations Claude would already know (e.g., explaining what graph databases are good for, listing database landscape). The 'when to use/not use' sections and database landscape overview add tokens without providing actionable guidance.	2 / 3
Actionability	Excellent executable code examples throughout - complete Python test fixtures, SurrealQL queries, connection pooling implementation, and caching patterns. All code is copy-paste ready with proper imports and context.	3 / 3
Workflow Clarity	Clear TDD workflow with explicit steps (write failing test → implement minimum → refactor → verify). The pre-implementation checklist provides explicit validation checkpoints, and the testing section includes unit, integration, and performance test patterns.	3 / 3
Progressive Disclosure	References external files (references/modeling-guide.md, references/query-optimization.md) appropriately, but the main document is quite long with extensive inline content that could be split. The 7 modeling patterns section alone is substantial and could be a separate reference file.	2 / 3
	Total	10 / 12 Passed

Description

89%

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 solid skill description with explicit 'Use when...' triggers and good domain-specific terminology. The main weakness is the use of vague qualifiers like 'Expert in' and 'deep knowledge of' rather than listing concrete actions the skill performs. The description would benefit from replacing abstract expertise claims with specific capabilities.

Suggestions

Replace 'Expert in graph database design and development with deep knowledge of' with concrete action verbs like 'Designs graph schemas, writes traversal queries, optimizes graph performance'

Convert 'Specializes in SurrealDB but applies generic graph database concepts' to actionable capabilities like 'Supports SurrealDB syntax and generic graph patterns for Neo4j, ArangoDB, etc.'

Dimension	Reasoning	Score
Specificity	Names the domain (graph databases) and mentions some actions like 'designing graph schemas, optimizing graph queries, implementing complex relationships', but uses vague terms like 'Expert in' and 'deep knowledge of' which are abstract rather than concrete actions.	2 / 3
Completeness	Clearly answers both what ('graph database design and development with deep knowledge of graph modeling, traversals, query optimization, and relationship patterns') and when ('Use when designing graph schemas, optimizing graph queries, implementing complex relationships, or building graph-based applications').	3 / 3
Trigger Term Quality	Good coverage of natural terms users would say: 'graph database', 'graph modeling', 'traversals', 'query optimization', 'relationship patterns', 'SurrealDB', 'graph schemas', 'graph queries', 'graph-based applications' - these are terms users would naturally use when needing this skill.	3 / 3
Distinctiveness Conflict Risk	Clear niche focused on graph databases specifically, with SurrealDB specialization mentioned. The triggers are specific to graph concepts and unlikely to conflict with general database or other skills.	3 / 3
	Total	11 / 12 Passed

Validation

75%

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 — 12 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (1268 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata' field is not a dictionary	Warning
license_field	'license' field is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	12 / 16 Passed

Repository: martinholovsky/claude-skills-generator
Commit: 1086ef2

Reviewed: 5 months ago

Table of Contents

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