Content
78%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.
The content is highly actionable and well-structured with clean progressive disclosure to real reference files. It is somewhat verbose for an overview, and the workflow would benefit from explicit validation checkpoints.
Suggestions
Trim the 'Quick Reference' section or fold it into the earlier sections to remove duplicated code and reduce token load.
Add explicit validation/checkpoint notes in the 'Common Workflow Pattern' (e.g., verify the graph loaded correctly before analysis, confirm connectivity assumptions before shortest-path calls).
Move the bulk of the inline visualization and generator examples into the reference files, keeping only one representative example per section in the overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lengthy (~450 lines) with mostly efficient code, but the 'Quick Reference' section largely repeats examples already shown and some explanatory prose could be tightened. | 3 / 5 |
Actionability | Throughout the skill, executable, copy-paste-ready Python covers the common cases (creation, algorithms, generators, I/O, visualization) with concrete API calls. | 5 / 5 |
Workflow Clarity | A 'Common Workflow Pattern' sequences five clear steps (Create/Load → Examine → Analyze → Visualize → Export), but validation checkpoints between steps are implicit rather than explicit. | 4 / 5 |
Progressive Disclosure | Clear overview body points to five well-signaled, one-level-deep references (graph-basics, algorithms, generators, io, visualization), all of which exist as real files, with detail appropriately split out. | 5 / 5 |
Total | 17 / 20 Passed |