Content
81%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-structured, actionable workflow with strong validation gates and externalized scripts. Minor conciseness trims and splitting dense policy tables into reference files would push it to top marks.
Suggestions
Trim explanatory asides like the virtualenv note in 'Required Scripts' to keep the body lean.
Move the target-repository and allowed-values tables into a references/ file, keeping SKILL.md as an overview with one-level links.
Consider providing a concrete example skill_dir value or a discovery snippet so the script commands are closer to copy-paste ready.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient instructions, tables, and command/JSON examples for a complex workflow, with a few over-explanatory asides (e.g. the virtualenv note) that could be trimmed but are not severely padded. | 4 / 5 |
Actionability | Concrete script invocations with flags, full JSON draft templates, and allowed-value tables give mostly executable guidance; necessary placeholders like <skill_dir> and <用户原话> keep it just short of copy-paste ready. | 4 / 5 |
Workflow Clarity | Six clearly sequenced steps with explicit validation checkpoints (success=false -> stop, preview rejection -> ask, explicit "确认" gate) and a re-collect feedback loop for an outward-facing submission operation. | 5 / 5 |
Progressive Disclosure | Scripts are appropriately externalized under scripts/ (verified: 4 real .py files) with one-level, clearly signaled references and well-organized sections; some policy detail lives inline rather than split into reference docs. | 4 / 5 |
Total | 17 / 20 Passed |