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 historical-interview-scriptsgit 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/historical-interview-scripts)<a href="https://agentmods.dev/skills/ufy2024/auc/historical-interview-scripts"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/historical-interview-scripts/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-interview-scripts"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/historical-interview-scripts.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 | $0.00044 | $0.04262 |
| Opus 5 | $0.00022 | $0.02131 |
| Sonnet 5 | $0.00009 | $0.00852 |
| Haiku 4.5 | $0.00004 | $0.00426 |
Grade A, and why
historical-interview-scripts 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 — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
历史名人访谈短视频文案生成 Skill
任务目标
- 本 Skill 用于：创作"现代访谈秀"形式的历史名人虚构访谈短视频文案
- 能力包含：
- 古今反差与喜剧效果构建
- 网络热梗与历史元素的融合
- 短视频结构化文案输出
- 触发条件：用户要求创作历史人物访谈、趣味历史内容、短视频文案等
操作步骤
标准流程
-
理解需求
- 确定访谈的历史人物（可参考 references/historical-characters.md）
- 明确核心访谈主题或槽点
- 确定视频风格倾向（吐槽、脑洞、互怼等）
-
素材准备
- 根据人物特点从 references/historical-characters.md 提取历史背景
- 从 references/internet-memes.md 选择合适的网络热梗
- 参考 references/script-examples.md 的创作范式
-
文案创作
- 设计视频标题/封面文案，带话题标签
- 编写本期嘉宾与核心梗概
- 创作开场主持人串词
- 撰写访谈正文（主持人提问 + 名人爆笑回答）
- 设计结束与互动引导
-
质量检查
- 验证幽默感是否自然（不生硬堆梗）
- 确保历史人物特点不被过度歪曲
- 检查画面感和分镜可行性
可选分支
- 当用户指定人物：优先使用该人物素材，按标准流程创作
- 当用户要求脑洞大开：可设计跨时空联动（如李白与杜甫CP、李清照吐槽苏轼）
- 当用户要求系列创作：可规划多期人物，形成连贯主题
基础模式：文案创作协作（4智能体）
当系统支持多智能体编排但仅支持文本生成时，可采用以下协作架构提升文案创作质量：
智能体角色分工
-
历史研究专家智能体 (Historian Agent)
- 职责：历史人物背景研究、核心形象提取、史实边界界定
- 输入：用户指定或系统推荐的历史人物列表
- 输出：结构化人物档案（核心事迹、性格特点、可调侃点、禁忌边界）
- 详细指导：见 references/agent-roles.md
-
热梗融合分析师 (Meme Analyst Agent)
- 职责：分析网络热度趋势，匹配历史人物与流行梗的融合点
- 输入：历史人物档案 + 实时/经典网络热梗库
- 输出：梗-人适配矩阵，每个历史人物配3-5个最匹配的热梗
- 详细指导：见 references/agent-roles.md
-
剧本创作大师 (Scriptwriter Agent)
- 职责：基于前两个智能体的输出，创作完整访谈剧本
- 输入：历史人物档案 + 梗融合建议 + 用户风格偏好
- 输出：符合格式要求的完整短视频文案
- 详细指导：见 references/agent-roles.md
-
质量审查与优化器 (QC Optimizer Agent)
- 职责：检查文案质量，确保符合约束条件，进行风格优化
- 输入：原始文案草案
- 输出：优化后的最终文案 + 优化建议报告
- 详细指导：见 references/agent-roles.md
协作流程
阶段一：并行研究（2-3分钟）
- 历史专家：研究人物A、B、C，生成人物档案
- 热梗分析师：分析当前热门梗，生成匹配建议
阶段二：协同创作（5分钟）
- 剧本创作大师接收人物档案和梗融合建议
- 创作初版访谈文案
阶段三：质量审查（3分钟）
- 质量审查智能体逐项检查（历史准确性/娱乐性/传播性/制作友好性）
- 如不达标，返回至创作阶段微调
- 如达标，生成最终文案
阶段四：输出与反馈（2分钟）
- 输出最终短视频文案
- 收集用户满意度数据，用于优化各智能体模型
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 · 278 lines · 44 tokens per session scan A 5ec003d1af01
historical-interview-scripts is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 4,262 once invoked, about $0.0002 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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