Content
68%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 body is highly actionable with comprehensive executable examples and good progressive-disclosure structure pointing to real reference files. Its main weaknesses are noticeable code duplication between Core Capabilities and Use Cases, and missing validation checkpoints on batch/destructive tree operations.
Suggestions
Collapse the duplicated code in Common Use Cases by referencing the relevant Core Capabilities section instead of restating the same tree_operations.py commands and PhyloTree workflow.
Add explicit validation checkpoints to batch and destructive workflows, e.g. "After pruning/rerooting, verify leaf count and topology with `python scripts/tree_operations.py stats` before proceeding."
Move the bulk of the inline visualization and clustering examples into the existing references/ files, keeping SKILL.md as a leaner overview with one or two quick-start snippets.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient and code-focused, but the Common Use Cases section substantially duplicates code already shown in Core Capabilities (e.g. tree_operations.py commands and the PhyloTree workflow), adding length that could be trimmed. | 3 / 5 |
Actionability | Provides extensive copy-paste-ready Python and bash examples covering tree manipulation, phylogenetic analysis, taxonomy, visualization, clustering, and comparison across the common cases. | 5 / 5 |
Workflow Clarity | Numbered sequences are present (e.g. Use Case 1 steps 1–6), but batch and destructive operations — Automated Tree Analysis deletes nodes across many trees, and pruning/rerooting modifies trees — lack validation checkpoints, capping this at 3. | 3 / 5 |
Progressive Disclosure | References to real one-level-deep files (references/api_reference.md, workflows.md, visualization.md and scripts/tree_operations.py, quick_visualize.py) are clearly signaled with descriptions, but the SKILL.md body is long and inline-heavy, keeping it just short of a clean 5. | 4 / 5 |
Total | 15 / 20 Passed |