Content
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body lays out a clear sequenced workflow and concrete usage modes, but it is padded with verbose examples and metadata, references module files that do not exist, and lacks an explicit validation feedback loop for its batch database writes.
Suggestions
Create the referenced modules/*.md files (domain_classifier, element_extractor, tagger, library_updater) or remove the dangling references so progressive disclosure actually resolves.
Add an explicit validate→fix→retry checkpoint after the database update step (e.g. verify dedup/element_id integrity, re-run on failure) to support the batch write operation.
Trim the verbose mock learning report and the version/maintainer/future-mode metadata to improve token efficiency.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The workflow and usage modes are usefully detailed, but the body carries unnecessary padding: emoji-heavy headers, version/maintainer metadata, a '未来: 可选全自动模式' placeholder, and a very long mock report example; mostly efficient but could be tightened. | 2 / 3 |
Actionability | Steps name concrete modules and outputs (e.g. 'modules/domain_classifier.md', '写入 elements.db'), and usage examples are concrete, but there is no executable code/SQL and the referenced module files do not exist, leaving guidance incomplete. | 2 / 3 |
Workflow Clarity | Steps 0–6 are clearly sequenced with per-step outputs, but the batch DB-write pipeline lacks an explicit validate→fix→retry feedback loop; per the scoring notes for batch/destructive operations this caps workflow_clarity at 2. | 2 / 3 |
Progressive Disclosure | Sections and a module table provide structure with one-level-deep references, but the referenced files (modules/*.md) are absent so navigation breaks, and a lot of content (full mock report, config) that could be separated is inline. | 2 / 3 |
Total | 8 / 12 Passed |