Content
73%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 multi-agent workflow with genuinely strong sequencing, validation checkpoints, and a proper feedback loop, supported by a real, one-level-deep reference bundle. Its weaknesses are verbosity — inline examples that duplicate an existing asset file and a corrupted trailing fragment — and an unspecified 'knowledge base' mechanism that the workflow depends on heavily.
Suggestions
Delete the corrupted trailing fragment ("3. 视觉设计智能体…" through the duplicated "关键参数" block at lines 458-465), which contradicts Example 3's stated 90-second duration with a '120秒视频方案'.
Replace the three ~40-line inline usage examples with a two-line pointer to assets/examples/example-output.md, keeping only one condensed example to illustrate the loop.
Make the 知识库 operational: specify where archived scripts/audio/visuals/final videos are stored (e.g. a directory layout or index-file format) and how matches are looked up, since Steps 1 and 5 both depend on it.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Most of the body is substantive domain guidance (persona rules, specs like "15秒脚本约30字", "BGM音量20-30%", JSON output contracts), but it is padded by ~120 lines of inline usage examples that duplicate the existing assets/examples/example-output.md, and the document ends with a corrupted leftover fragment (lines 458-465: "3. 视觉设计智能体…4. 数字人驱动智能体…生成120秒视频方案" plus a second conflicting "关键参数" block) that contradicts Example 3's own parameters above it. Above level 2 because the explanations are domain-specific, not concepts Claude already knows; below 4 because of the duplication and the incoherent tail. | 3 / 5 |
Actionability | Guidance is concrete for an instruction-type skill: every step has a JSON output contract, quantified specs (9:16, "分辨率≥1080P", frame_rate 30, "口播音量100%、BGM音量20-30%", script word counts per duration), and pointers to real API options ("参考 references/tech-integration.md 中的语音合成API（Azure TTS、百度TTS等）" / "FFmpeg、剪映API"). Not a 5 because the pervasive '知识库' (knowledge base) mechanism is never made operational — no location, format, or storage/retrieval procedure is stated — leaving a key loop under-specified. | 4 / 5 |
Workflow Clarity | The five-agent pipeline is clearly sequenced as a closed loop (需求对接 → 脚本+口播 → 画面 → 音画合成 → 校验归档), and Step 5 provides exactly the anchor-5 pattern: an explicit 校验清单 checklist (人设统一, 口播质量, 画面质量, 音画同步, 内容合规), a feedback loop ("有优化需求则反馈对应智能体调整…迭代至达标后重新归档"), and a bounded recovery rule ("最多回溯2个层级"). | 5 / 5 |
Progressive Disclosure | The 资源索引 section signals each reference with its purpose and an explicit 'when to read' cue ("何时读取：在执行对应智能体任务前，参考其角色定义"), and the four references plus assets/examples all exist and are one level deep with no nested cross-references. Not a 5 because SKILL.md is more monolithic than an overview: the three long inline examples duplicate the dedicated assets/examples/example-output.md, and the per-agent JSON specs largely overlap references/agent-roles.md, content that belongs in the bundle files. | 4 / 5 |
Total | 16 / 20 Passed |