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by ysyecust · Codex Skill · ★ 228
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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lecture-to-notes 在线预览所有讲义和论文解读 → 两个 AI 驱动的学习工具： lecture-to-notes：将 YouTube / Bilibili 讲座视频转换为专业的中文 LaTeX 课程笔记和 PDF paper-to-html：将学术论文转换为结构化的中文 HTML 解读页面 视频 URL → LaTeX PDF 讲义 | 论文 → 自包含 HTML 解读 特性 多平台支持：YouTube 和 Bilibili（自动识别 URL） 字幕四级回退：CC 字幕 → YouTube 自动字幕（自动去重）→ Whisper 语音转写 → 纯视觉模式 字幕清洗：YouTube auto-subs 自动去重（通常去除 50% 重复行） 密集帧采样：每 15 秒采样 + contact sheet 批量审查，不遗漏关键画面 图文三方验证：每个配图写入前必须通过「帧画面 + 字幕内容 + 描述文字」三方一致性检查，防止图文不匹配 高信息密度写作：结构化章节、教学信号盒（核心概念/背景知识/常见误区）、时间溯源脚注 数学公式支持：准确转写 PPT 中的数学公式为 LaTeX display math + 符号解释 完整交付： 源文件 + 配图 + 编译好的 PDF 仓库结构 text . ├── README.md ├── LICENSE ├── scripts/ │ ├── cleansubs.py # YouTube 自动字幕去重 │ ├── correctsrt.py # Whisper SRT 词典级修正（数据驱动，快） │ ├── llmcorrectsrt.py # Whisper SRT 段级修正（LLM + 多模态，慢但更准） │ ├── verifyfigures.py # 图文三方验证（时间戳 × 字幕 × 画面） │ ├── preparecover.sh # 封面格式转换（webp/png → jpg） │ ├── smartcrop.py # 课件区域检测（实验性，实际流程中通常直接用全帧） │ └── whisperprompts/ # Whisper --initialprompt 术语表 │ ├── glossarynjuos.json # 词典修正的 wrong→right 对 │ └── njuos.txt # 引导 Whisper 正确转写专业术语 ├── docs/ │ ├── index.html # GitHub Pages 首页（讲义 + 论文卡片网格） │ ├── pdfs/ # 已发布的讲义 PDF │ └── papers/ # 已发布的论文解读 HTML └── skills/ └── lecture-to-notes/ ├── SKILL.md # Skill 主定义（适
| Stars | 228 |
| Forks | 23 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 58.6329262696229/100 |
| Last Updated | 2026-09-16 |
| Created | 2026-03-24 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~14k |
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lecture-to-notes is Turn YouTube/Bilibili lectures into smart-cropped LaTeX/PDF notes. Claude Code & Codex skill.. It is categorized as a Codex Skill with 228 GitHub stars.
lecture-to-notes is primarily written in Python.
You can find installation instructions and usage details in the lecture-to-notes GitHub repository at github.com/ysyecust/lecture-to-notes. The project has 228 stars and 23 forks, indicating an active community.
The top alternatives to lecture-to-notes on Agent Skills Hub include Skills-Manager, ai-maestro, upskill. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: