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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ufy2024/AuCnpx agentmods add skills/ufy2024/auc/historical-science-video-prodWrote 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/historical-science-video-prod)<a href="https://agentmods.dev/skills/ufy2024/auc/historical-science-video-prod"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/historical-science-video-prod/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/historical-science-video-prod"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/historical-science-video-prod.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 | $0.00056 | $0.01027 |
| Opus 5 | $0.00028 | $0.00513 |
| Sonnet 5 | $0.00011 | $0.00205 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
historical-science-video-prod 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.
What it actually says
历史科学类科普短视频自动化生成 Skill
任务目标
- 本 Skill 用于：自动化生成历史科学类3分钟科普短视频的全流程素材
- 能力包含：口播文案创作、分镜脚本规划、Veo2提示词生成、人物形象设计
- 触发条件：用户需要制作历史科学类科普视频，希望快速获得完整的素材包
前置准备
- 依赖说明：无需额外安装，依赖包已在 dependency 字段中声明
- 输入要求：用户提供科普主题、年代背景、核心科学结论
操作步骤
-
标准流程：
-
准备输入信息
- 确定科普主题（如"蝴蝶效应的起源"、"青霉素的发现"）
- 明确年代背景（如"1961年"、"1928年"）
- 提炼核心科学结论（如"初始条件的敏感性"）
-
执行生成脚本
- 调用
scripts/generate_video_materials.py处理... - 传入参数：--theme、--era、--core_conclusion、--output_dir
- 调用
-
查看输出素材
output/script.txt- 3分钟口播文案（约900-1100字）output/storyboard.md- 分镜脚本表（30-35个分镜）output/veo2_prompts.txt- Veo2提示词清单output/character_design.md- 人物形象规范
-
-
可选分支：
- 当 需要调整风格：在 references/style-guide.md 中修改复古风格参数
- 当 需要优化文案结构：在 references/script-structure.md 中调整时间分配
资源索引
- 必要脚本：见 scripts/generate_video_materials.py（用途与参数：生成全流程素材包，接收主题、年代、核心结论参数）
- 领域参考：见 references/multi-agent-architecture.md（何时读取：了解多智能体协作体系架构）
- 领域参考：见 references/style-guide.md（何时读取：调整复古风格参数时）
- 领域参考：见 references/veo2-prompt-template.md（何时读取：了解Veo2提示词标准格式）
- 领域参考：见 references/script-structure.md（何时读取：了解文案结构和时间分配）
注意事项
- 脚本会自动调用大模型生成内容，生成时间约1-2分钟
- 输出文件将保存到指定的 output_dir 目录
- 确保提供的核心科学结论准确无误，脚本会基于此生成文案
- 生成的分镜脚本严格遵循3分钟时长，每镜4-6秒
使用示例
- 功能说明：生成"蝴蝶效应的起源"主题的视频素材包
- 执行方式：脚本自动调用大模型生成
- 关键参数或指导要点：主题、年代、核心结论
- 简单示例代码或命令：
python scripts/generate_video_materials.py \
--theme "蝴蝶效应的起源" \
--era "1961年" \
--core_conclusion "初始条件的微小变化会导致巨大差异，无法长期预测天气" \
--output_dir "./output"
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 · 79 lines · 56 tokens per session scan A 80d02d376313
historical-science-video-prod is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 1,027 once invoked, about $0.0003 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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