Content
72%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 content is well-structured and actionable with an excellent progressive-disclosure layout pointing to real bundle files, but the batch generation workflow lacks validation/feedback checkpoints.
Suggestions
Add a validation step after script execution, e.g. verify output/script.txt word count is 900-1100 and that all four output files exist before declaring success.
Add a feedback loop for LLM failures (retry on empty/low-quality output, re-prompt if word count is out of range).
Document expected exit codes or error handling for the generate_video_materials.py script so failures are diagnosable.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean with short, well-organized sections and no padding explaining concepts Claude already knows; a few lines in 注意事项 could be trimmed but overall efficient. | 4 / 5 |
Actionability | Provides a concrete, copy-paste-ready bash command with all flags (--theme, --era, --core_conclusion, --output_dir) and names the concrete output files; minor gaps in variant/edge-case coverage keep it from a 5. | 4 / 5 |
Workflow Clarity | Numbered steps (准备输入 → 执行脚本 → 查看输出) are clearly sequenced, but this is a batch LLM-generation operation with no validation or verification checkpoints (e.g., confirm outputs exist, check word count, retry on failure), which caps workflow_clarity at 3 per the batch-operation guideline. | 3 / 5 |
Progressive Disclosure | Verified against the actual bundle (references/ has 4 real files, scripts/ has generate_video_materials.py, assets/ has output-templates): the 资源索引 section signals each one-level-deep reference with explicit '何时读取' guidance and the body is a clear overview pointing to details. | 5 / 5 |
Total | 16 / 20 Passed |