Content
61%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 body is well-structured and highly actionable, with commands that were verified flag-by-flag against the bundled scripts. Its weaknesses are notable redundancy (repeated Coze Bot benefits, duplicated parameter lists, an example section that re-runs the standard flow), missing validation checkpoints in a batch workflow, and two doc/code mismatches (URL input, resolution default). A hardcoded real-looking Coze API key and Bot ID in 前置准备 is also a security liability that does not belong in a skill file.
Suggestions
Add explicit validation between steps — e.g. after 抽帧 verify frame count > 0 before calling the analysis API, and check analysis.json parses before integrating — and an error-recovery note (what to do when the API returns errors or empty results); batch workflows cannot score above 3 without this.
Remove duplication: keep one Coze Bot benefits statement, one parameter list, and cut 示例1 (it repeats the standard flow verbatim); push the JSON output schema and model-comparison table into references/analysis-guide.md and link to them.
Fix the doc/code mismatches: --input does not support URLs (video_frame_extractor.py has no URL handling) and --resolution defaults to keeping original resolution, not 1080P; also strip the hardcoded Coze Bot ID and API key from SKILL.md and reference environment variables only.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient — no explanations of concepts Claude already knows — but carries clear redundancy: the Coze Bot advantages are stated twice (「Coze Bot的优势」 in 前置准备 and 「Coze Bot优势」 in 技术说明), the frame/analysis parameters are documented in both 操作步骤 and 可选参数, and 使用示例 示例1 nearly duplicates the 步骤1/步骤2 command blocks verbatim. It fits 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than the 4 anchor, because entire repeated sections (not just a few lines) could be cut. | 3 / 5 |
Actionability | Guidance is concrete and mostly copy-paste ready: full bash invocations with every flag, verified against the scripts' actual argparse definitions (--input/--interval/--max_frames/--start_time/--end_time/--resolution in video_frame_extractor.py:153-159; --image/--image_dir/--prompt/--output in coze_bot_client.py:287-295; --detail/--batch_size in visual_analyzer.py:253-267), plus a concrete JSON output schema. Not a 5 because two documented claims don't match the code: the body says --input accepts a URL and that --resolution defaults to 1080P, but video_frame_extractor.py contains no URL handling and defaults to keeping original resolution — executable commands with minor factual gaps. | 4 / 5 |
Workflow Clarity | The three-step sequence (抽帧 → 视觉分析 → 结果整合) is clearly listed with two alternative analysis paths, but there are no validation checkpoints between steps (e.g., verify frames were actually produced before batch analysis, check the analysis JSON is well-formed), and no error-recovery loop. This is a batch-operation skill, and the rubric explicitly caps workflow_clarity at 3 when batch workflows lack validation — the soft note 「建议先用小规模抽帧测试」 is advice, not a checkpoint. Not a 2, because the sequence itself is coherent, well-ordered, and parameterized. | 3 / 5 |
Progressive Disclosure | Structure is good: SKILL.md acts as an overview with a dedicated 资源索引 section linking the three real scripts and the one real reference (references/analysis-guide.md, confirmed present), and the reference is one level deep and clearly signaled. Minor gaps keep it below 5: some material inlined in the body (the full JSON output-format block, the model-comparison detail, the Coze Bot marketing) belongs in references/analysis-guide.md, and the 技术说明 section repeats overview-level information instead of deferring to the reference. | 4 / 5 |
Total | 14 / 20 Passed |