Content
80%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill body is dense, actionable, and token-efficient, with executable examples and clear tool documentation. Its main gaps are the absence of validation/verification checkpoints for destructive and batch operations, and a monolithic structure that could benefit from splitting the API reference into a referenced file.
Suggestions
Add validation/verification checkpoints for destructive and batch operations — e.g., call list_events to confirm the target before delete_event, and verify batch add_event results before proceeding.
Split the full per-action parameter tables into a separate REFERENCE.md and keep SKILL.md as a concise overview with clearly signaled one-level-deep references, improving progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean reference material (parameter tables, a decision tree, executable examples) with no padding explaining concepts Claude already knows; every section earns its place. | 3 / 3 |
Actionability | It provides fully executable `tool.call(...)` Python examples, a concrete `mkdir -p` shell command, and copy-paste-ready parameter tables, matching the score-3 anchor. | 3 / 3 |
Workflow Clarity | Steps are sequenced and a decision tree maps requests to actions, but destructive (`delete_event`) and batch ("batch or one-by-one") operations lack explicit validation/verification checkpoints, capping this at 2 per the rubric. | 2 / 3 |
Progressive Disclosure | The 235-line body is monolithic with the full API reference inline and no bundle files or one-level-deep references; content that could be split into a separate reference file is inline, matching the score-2 anchor. | 2 / 3 |
Total | 10 / 12 Passed |