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/infinitetalk-infinitetalkWrote 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/infinitetalk-infinitetalk)<a href="https://agentmods.dev/skills/ufy2024/auc/infinitetalk-infinitetalk"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/infinitetalk-infinitetalk/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/infinitetalk-infinitetalk"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/infinitetalk-infinitetalk.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 | $0.00059 | $0.01561 |
| Opus 5 | $0.00030 | $0.00781 |
| Sonnet 5 | $0.00012 | $0.00312 |
| Haiku 4.5 | $0.00006 | $0.00156 |
Grade A, and why
infinitetalk 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- infinitetalk — 97% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
InfiniteTalk - 音频驱动视频生成
任务目标
- 本 Skill 用于：将音频（语音）转换为同步的说话人视频，支持从单张图片或现有视频生成音频驱动的说话视频
- 能力包含：
- Image-to-Video：从单张图片生成音频驱动的说话视频
- Video-to-Video：对现有视频进行音频驱动的重配音
- 多维度同步：唇形、头部运动、身体姿态、面部表情与音频精准对齐
- 无限时长：支持无限制时长的视频生成
- 低显存适配：支持量化、模型卸载等显存优化方案
- 触发条件：当需要生成音频驱动的数字人视频、视频配音、虚拟主播内容时使用
前置准备
- 模型下载：在使用本 Skill 前，必须先下载所需的模型权重文件，具体步骤见 references/model_download.md
- 硬件要求：
- GPU：推荐使用 16GB+ 显存的 GPU（可使用量化方案适配低显存设备）
- 内存：建议 32GB+ 系统内存
- 磁盘空间：至少 50GB 可用空间（模型权重约 30GB）
- 环境配置：详细依赖安装见 references/environment_setup.md
操作步骤
模式一：Image-to-Video（图片生成视频）
-
准备输入
- 确保有一张清晰的人脸图片作为输入
- 准备音频文件（支持 mp3、wav 等格式）
- 可选：使用 TTS 功能从文本生成音频
-
执行生成
- 调用
scripts/infer_infinitetalk.py进行推理 - 参数说明：
input_path: 输入图片路径audio_path: 驱动音频路径（或提供text使用 TTS）output_path: 输出视频路径mode:clip（单段）或streaming（长视频）size:infinitetalk-480（480P）或infinitetalk-720（720P）sample_steps: 采样步数（默认 40）sample_audio_guide_scale: 音频引导强度（默认 4.0）
- 调用
-
验证输出
- 检查生成的视频是否同步良好
- 确认唇形、头部动作与音频匹配
- 如有异常，调整
sample_audio_guide_scale参数
模式二：Video-to-Video（视频重配音）
-
准备输入
- 准备参考视频文件
- 准备目标音频文件
-
执行生成
- 使用相同的脚本，但
input_path指向视频文件 - 脚本会自动提取视频的首帧作为参考
- 使用相同的脚本，但
-
处理长视频
- 使用
streaming模式生成无限时长视频 - 通过
motion_frame参数控制驱动帧长度（默认 9）
- 使用
模式三：使用 TTS 生成音频
-
文本转语音
- 提供待合成的文本内容
- 指定声音模型（Kokoro-82M）
- 脚本会自动生成音频文件
-
生成视频
- 使用生成的音频驱动视频生成
- 支持双人对话模式（使用标记
(s1)和(s2)区分说话人）
资源索引
- 核心脚本：见 scripts/infer_infinitetalk.py（音频驱动视频生成推理）
- 环境配置：见 references/environment_setup.md（依赖安装和系统配置）
- 模型下载：见 references/model_download.md（模型权重下载指南）
- 使用示例：见 references/usage_examples.md（典型场景和命令示例）
注意事项
- 模型权重较大（约 30GB），首次使用需要提前下载
- 建议使用高显存 GPU（16GB+），低显存设备可使用量化方案
- 输入音频建议采样率为 16000Hz，脚本会自动进行响度归一化
- 输入图片/视频应包含清晰的人脸区域
- 生成速度取决于 GPU 性能，480P 分辨率下生成 1 秒视频约需 5-10 秒
- 如遇到显存不足错误，可尝试：
- 降低分辨率（使用
size=infinitetalk-480） - 启用量化（添加
--quant int8参数） - 启用模型卸载（添加
--offload_model true）
- 降低分辨率（使用
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 · 134 lines · 59 tokens per session scan A e4e693506403
infinitetalk is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 59 tokens to every session and 1,561 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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