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 high-quality, highly actionable skill that provides a clear end-to-end workflow for running Neo4j graph algorithms in Snowflake. Its greatest strengths are the executable SQL examples at every step, the detailed casting rules table, and the comprehensive troubleshooting section. The main weakness is that the document is quite long and could benefit from splitting reference-heavy sections (algorithm tables, privilege setup, installation) into separate files for better progressive disclosure.
Suggestions
Move the algorithm tables and detailed privilege setup SQL into separate referenced files (e.g., references/algorithms.md already referenced, plus references/setup.md) to reduce the main skill's length and improve progressive disclosure.
Ensure the referenced 'references/algorithms.md' file actually exists in the bundle, as it's currently unverifiable.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is generally well-written and avoids explaining basic concepts Claude knows, but it's quite long (~400+ lines) with some sections that could be tightened. The algorithm tables are comprehensive but borderline reference material that could live in a separate file. The casting rules table and orientation guidance are valuable and earn their place, but some prose (e.g., 'This is the step that matters most', 'This is the flow that works') is slightly padded. | 2 / 3 |
Actionability | Excellent actionability throughout — executable SQL examples for every step (DDL inspection, view creation, CALL syntax, result joining), explicit casting rules with exact syntax, complete privilege setup scripts, and concrete naming conventions. The examples are copy-paste ready with clear placeholder conventions. | 3 / 3 |
Workflow Clarity | The 4-step end-to-end flow is clearly sequenced with explicit validation points. The skill warns against skipping data preparation, includes a troubleshooting table for common failure modes, provides a checklist at the end, and the casting rules table serves as a validation reference. The 'lowest-common-denominator policy' for column inclusion is a thoughtful safety guardrail. | 3 / 3 |
Progressive Disclosure | The skill references 'references/algorithms.md' for detailed algorithm parameters, which is good progressive disclosure, but no bundle files were provided to verify this exists. The main file is quite long and the algorithm tables, installation instructions, and privilege setup could arguably be split into separate reference files. The external doc links are well-organized in the Further Reading section. | 2 / 3 |
Total | 10 / 12 Passed |