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dream-video-prompt-generator

小省导购员数字人带货版即梦视频提示词生成系统,基于四大智能体协同(提示词生成师、质量管控师、知识库运维师、跨环节适配师),按照"主体+运动+场景+(镜头语言+光影+氛围)"公式输出中英文双版提示词,适配5s短视频。确保人物一致性、视觉连贯性、情绪连贯性,支持知识库智能复用和跨工具适配(Suno音乐、AI绘画),为数字人带货视频提供高质量提示词生成服务。

63

Quality

76%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/dream-video-prompt-generator/dream-video-prompt-generator/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

85%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable with a well-sequenced, validated workflow and excellent one-level-deep progressive disclosure of a real bundle. Its main weakness is conciseness, as schemas and the workflow are restated across multiple sections.

Suggestions

Collapse the duplicated per-scene JSON schemas and the three near-identical '使用示例' workflow restatements into a single canonical example plus a compact parameter table.

Remove the '注意事项' restatement of principles already covered in '操作步骤', or replace it with a short cross-reference back to the relevant step.

Trim the full worked output example to one representative scene with a pointer to assets/examples/sample-prompts.md for the complete set.

DimensionReasoningScore

Conciseness

The body is mostly efficient and domain-specific, but repeats the per-scene JSON schema, the full worked example, and restates the workflow/principles across "操作步骤", "注意事项", and three "使用示例" blocks that could be tightened.

2 / 3

Actionability

Provides copy-paste-ready, fully specified guidance: an explicit prompt formula, concrete JSON output schemas for every step, a verification checklist, and a worked example with real Chinese/English prompt text.

3 / 3

Workflow Clarity

The five-step sequence is clearly numbered with an explicit quality-check validation step, a pass/fail checklist, and a defined validate→fix→retry feedback loop capped at 2 iterations.

3 / 3

Progressive Disclosure

A clear resource index points one-level-deep to six verified reference files and one asset, each annotated with a '何时读取' (when to read) trigger, keeping heavy detail (scene templates, role definitions) appropriately offloaded.

3 / 3

Total

11

/

12

Passed

Description

67%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific and clearly occupies a distinct niche, but it reads as a dense system summary rather than a natural user-facing trigger and omits an explicit "Use when..." clause.

Suggestions

Add an explicit 'Use when...' clause (e.g., 'Use when the user needs to generate Jimeng/JiMeng video prompts for the Xiaosheng digital-human live-commerce persona') to lift completeness and trigger-term quality.

Lead with the concrete trigger keywords a user would actually say ('即梦视频提示词', '数字人带货提示词', 'JiMeng video prompt') before the system architecture, and tighten the four-agent enumeration.

Rewrite in a concise third-person action voice focused on inputs and outputs, trimming the architectural detail that a user would not naturally utter.

DimensionReasoningScore

Specificity

Names multiple concrete capabilities — "四大智能体协同", "主体+运动+场景+(镜头语言+光影+氛围)" formula, "中英文双版", and cross-tool Suno/AI-painting adaptation — going beyond a single domain action.

3 / 3

Completeness

Clearly states what the skill does, but "when to use it" is only implied with no explicit "Use when..." trigger guidance, which per the judging guidelines caps completeness at 2.

2 / 3

Trigger Term Quality

Contains relevant domain keywords ("即梦视频提示词", "数字人带货", "提示词生成") a user might say, but the phrasing is dense system-naming rather than crisp natural user utterance and lacks a clear trigger clause.

2 / 3

Distinctiveness Conflict Risk

The niche is highly specific (Jimeng video prompts for a named digital-human live-commerce persona) with distinct triggers, making conflict with other skills unlikely.

3 / 3

Total

10

/

12

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 1 deeper-than-1-level

Warning

referenced_paths_exist

Referenced path issues: 2 deeper-than-1-level

Warning

Total

14

/

16

Passed

Repository
anbeime/skill
Reviewed

Table of Contents

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