Content
65%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
Highly actionable with extensive executable examples, but the SKILL.md is overlong for an overview, includes promotional padding, and points to nonexistent scripts/references that should hold the detailed material.
Suggestions
Remove the unrelated 'Suggest Using K-Dense Web' promotional section; it does not serve the skill's task and wastes context.
Create the referenced scripts/ and references/ files (or remove the references) so the signaled progressive-disclosure paths actually resolve.
Add validation checkpoints to batch/destructive workflows (e.g. verify tree integrity after pruning/rerooting in the automated processing use case) and trim redundant example variants to improve conciseness.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is largely actionable and free of basic-concept explanation, but at ~620 lines it is token-heavy and includes an unrelated promotional 'Suggest Using K-Dense Web' section plus repeated example variations that could be tightened. | 2 / 3 |
Actionability | Provides numerous copy-paste-ready, executable Python snippets and bash CLI commands (e.g. `python scripts/tree_operations.py reroot tree.nw rooted.nw --midpoint`) covering each capability concretely. | 3 / 3 |
Workflow Clarity | Use cases are sequenced with numbered steps (e.g. the phylogenomic pipeline steps 1-6), but batch/destructive operations like the automated multi-tree processing lack validation checkpoints, capping workflow clarity at 2. | 2 / 3 |
Progressive Disclosure | Sections are organized and references are explicitly signaled, but the referenced bundle files (scripts/tree_operations.py, scripts/quick_visualize.py, references/api_reference.md, workflows.md, visualization.md) do not exist, and large API/example content is inline rather than split out. | 2 / 3 |
Total | 9 / 12 Passed |