AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ufy2024/AuC --skill dream-video-prompt-generatorgit clone --depth 1 https://github.com/ufy2024/AuCWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ufy2024/auc/dream-video-prompt-generator)<a href="https://agentmods.dev/skills/ufy2024/auc/dream-video-prompt-generator"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/dream-video-prompt-generator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ufy2024/auc/dream-video-prompt-generator"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/dream-video-prompt-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00144 | $0.03971 |
| Opus 5 | $0.00072 | $0.01985 |
| Sonnet 5 | $0.00029 | $0.00794 |
| Haiku 4.5 | $0.00014 | $0.00397 |
Grade A, and why
dream-video-prompt-generator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.
小省导购员数字人带货即梦视频提示词生成系统
任务目标
- 本 Skill 用于:生成小省导购员数字人带货视频的即梦视频生成提示词
- 能力包含:
- 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
- 提示词公式驱动:主体+运动+场景+(镜头语言+光影+氛围)
- 中英文双版对应:适配国际AI工具需求
- 一致性保障:人物一致性、视觉连贯性、情绪连贯性
- 知识库智能复用:同类需求直接调取,新增需求迭代优化
- 跨工具适配:联动Suno音乐生成、AI绘画生成
- 触发条件:用户需要生成小省导购员数字人带货视频的提示词
前置准备
- 无需特殊依赖
- 准备带货视频信息:
- 场景类型(产品推荐、价格对比、促销活动等)
- 情绪基调(热情、专业、亲切、紧迫等)
- 视频时长(默认5s)
- 分镜幕次(默认5幕)
操作步骤
标准工作流程(四大智能体协同)
步骤1:需求对接与知识库核查(智能体3:知识库运维师)
职责:优先核查知识库,同类需求直接调取,新需求启动协作
- 需求识别:识别场景类型、情绪基调、分镜幕次
- 知识库核查:查询是否存在同类场景的提示词模板
- 直接复用:如匹配,直接调取并返回提示词
- 新增处理:如不匹配,传递给提示词生成师启动创作
输出格式:
{
"demand_type": "产品推荐/价格对比/促销活动",
"emotion_tone": "热情/专业/亲切/紧迫",
"scene_count": 5,
"knowledge_base_match": "true/false",
"existing_prompts": "如匹配,返回已存在提示词;如不匹配,返回null"
}
步骤2:提示词生成(智能体1:提示词生成师)
职责:按公式输出即梦提示词,严格遵循一致性
提示词公式:
主体(主体描述)+ 运动 + 场景(场景描述)+(镜头语言 + 光影 + 氛围)
核心要求:
- 主体一致性:所有分镜沿用核心角色固定描述
- 情绪适配性:光影、氛围匹配对应幕次情绪
- 中英文双版:生成中英文对照版本
- 细节具体:动作连贯,适配5s短视频
- 延伸细节:规避AI独立生成的上下文断层问题
输出格式(每幕):
{
"scene_number": 1,
"scene_name": "场景名称",
"duration": "5s",
"chinese_prompt": {
"subject": "主体描述",
"movement": "运动描述",
"scene": "场景描述",
"shot_language": "镜头语言",
"light": "光影",
"atmosphere": "氛围",
"full_prompt": "完整中文提示词"
},
"english_prompt": {
"subject": "Subject description",
"movement": "Movement description",
"scene": "Scene description",
"shot_language": "Shot language",
"light": "Light",
"atmosphere": "Atmosphere",
"full_prompt": "Complete English prompt"
},
"emotion_tone": "适配情绪",
"consistency_check": {
"character_description": "与核心角色固定描述一致",
"visual_elements": "视觉元素连贯",
"emotion_progression": "情绪递进自然"
}
}
步骤3:质量核查(智能体2:质量管控师)
职责:核查提示词的景别、光影、情绪匹配度
核查清单:
- 主体一致性:同角色外貌描述统一(年龄、发型、服饰、气质)
- 景别合理性:镜头语言符合场景需求
- 光影匹配度:光影适配情绪基调
- 情绪连贯性:从第一幕到第五幕的情绪递进自然
- 视觉连贯性:视觉线索贯穿五幕(色调、场景元素)
- AI出图/视频要求:细节具体,动作连贯,适配5s短视频
- 中英文对应:中英文版本语义一致
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 386 lines · 144 tokens per session scan A 699e9cd958a8
dream-video-prompt-generator is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 144 tokens to every session and 3,971 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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