Content
92%Weight 40%Scale 1-3Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A highly actionable, concise skill body with excellent executable examples and a well-sequenced, risk-aware batch workflow. Its only gap is progressive disclosure: everything lives in one monolithic file with no bundled reference files to offload detail.
Suggestions
Move the detailed parameter tables and NetLog/risk-report material into a bundled reference file (e.g. references/cli-reference.md) and link to it from SKILL.md to achieve one-level-deep progressive disclosure.
Consider extracting the batch anti-风控 recipe into a reference so the main body stays a lean overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and token-efficient: commands, parameter tables, and field descriptions with no padding or explanation of concepts Claude already knows. | 3 / 3 |
Actionability | Every workflow ships copy-paste-ready `python scripts/cli.py …` commands with concrete flags, values, and a real batching/rate-limit recipe. | 3 / 3 |
Workflow Clarity | Multi-step batch detail retrieval is explicitly sequenced with a validation/risk checkpoint (3-feed groups + randomized sleep) and an input-judgment priority list; failure handling is enumerated. | 3 / 3 |
Progressive Disclosure | Well-organized single-file structure, but there are no references, scripts, or assets bundled, so content that could be split out (full parameter tables, NetLog/risk-report detail) stays inline; no one-level-deep references exist to signal navigation. | 2 / 3 |
Total | 11 / 12 Passed |