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.
84
83%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
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 |
Implementation
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 |
Validation
75%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
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