Content
92%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-architected skill body: executable dual-language examples, an explicitly validated workflow with recovery guidance, and clean progressive disclosure into two real reference files. Only marginal trimming of a few inline glosses would improve conciseness further.
Suggestions
Trim a handful of one-line definitional glosses (e.g. betweenness 'identifies bottleneck/bridge nodes', Node Similarity 'Jaccard similarity from common neighbors') since Claude already knows these concepts.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean and code-heavy, assuming Claude's competence (no preamble explaining what PageRank or a graph is), with operational inline notes that earn their place; a few short glosses like 'identifies bottleneck/bridge nodes' and 'Jaccard similarity from common neighbors' could be trimmed but are minor. | 4 / 5 |
Actionability | Provides copy-paste-ready executable Cypher and Python snippets across projection, every execution mode, and each core algorithm, plus a complete end-to-end FastRP→KNN pipeline — fully covering the common cases. | 5 / 5 |
Workflow Clarity | Clear sequenced 'Full Workflow' with explicit validation checkpoints (gds.version pre-flight, memory estimation before large ops, 'stream to verify → mutate to chain → write to persist'), a confirmation gate before write-cypher, a Common Errors recovery table, and a closing checklist. | 5 / 5 |
Progressive Disclosure | SKILL.md stays an overview with well-signaled one-level-deep references to real bundle files ([references/algorithms.md], [references/graph-projection.md]) for the full catalog and projection deep-dive, keeping the inline body navigable and appropriately split. | 5 / 5 |
Total | 19 / 20 Passed |