---

name: bilibili-video-analyzer description: Analyzes Bilibili academic/educational videos to extract knowledge points and generate clean-style study notes with screenshots. Use this skill when users provide a Bilibili video link and want to generate a professional learning report in card format with core concepts, detailed explanations, key points, and automatically captured screenshots.

Bilibili Video Analyzer

Overview

This skill analyzes Bilibili academic and educational videos to generate professional clean-style learning notes (清洁版学习笔记). It automates the complete workflow from video download and transcription to AI-powered content analysis and report generation with key screenshots.

📚 Extended Resources:

• 📖 Best Practices Guide - 全面的最佳实践指南
• ✅ Quality Checklist - 28项质量检查清单
• 📁 References Index - 参考文档导航

When to Use This Skill

Trigger phrases:

• "分析这个B站视频: [link]"
• "帮我总结这个视频的知识点"
• "生成这个视频的学习报告"
• "提取这个视频的关键内容"

---

Installation

Prerequisites

• Python 3.7+
• FFmpeg (for video processing)
• Sufficient disk space (~1-2GB per video analysis)

Install from PyPI

pip install railgun-bili-tools

Verify Installation

bili-dl --version

Install FFmpeg

macOS:

brew install ffmpeg

Ubuntu/Debian:

sudo apt install ffmpeg

Windows: Download from ffmpeg.org and add to PATH

---

Workflow

7-Step Automated Process:

Step 1: Login Check

bili-dl status
# If not logged in: bili-dl login

Step 2: Parse Video Information

Extract metadata (title, uploader, duration) using BilibiliParser

Step 3: Download Video

bili-dl download <video_url> --quality 1080p --output <output_dir>

Step 4: Transcribe Audio

bili-dl transcribe <video_path> --model medium --srt

Step 5: AI Content Analysis ⭐

Claude analyzes the subtitle content and extracts:

• 6-10 核心知识点 (knowledge point cards)
• Each point includes:
  • title (10-15字)
  • core_concept (20-30字核心概念)
  • details (200-400字详细说明,Markdown格式)
  • key_points (3-5个关键要点)
  • timestamp (视频时间戳)

Output JSON Structure:

{
  "summary": "视频总览(100-200字)",
  "knowledge_points": [...],
  "key_screenshots": [
    {"timestamp": 280, "description": "截图描述", "reason": "选择原因"}
  ],
  "knowledge_framework": "知识体系结构",
  "practical_value": "实践价值说明",
  "learning_suggestions": ["学习建议1", "学习建议2", ...]
}

Step 6: Capture Screenshots

# 使用 scripts/screenshot_tool.py
ffmpeg -y -ss <timestamp> -i <video_path> -vframes 1 -q:v 2 <output.jpg>

Step 7: Generate Report

Use scripts/report_generator.py to create clean-style learning notes

Output Format:

• 标题: 《{视频标题}》学习笔记
• 概览: 视频时长 + 知识点数量
• 核心内容: 📌 知识点卡片(核心概念 + 详细说明 + 关键要点 + 配图)
• 全文总结: 核心知识框架 + 实践价值 + 学习建议

Quality Standards

Based on successful case (BV1ms4y1Y76i):

Metric	Standard	Example
知识点数	6-10个	7个
单点字数	200-400字	平均320字
核心概念	20-30字	简洁有力
关键要点	3-5个/点	便于记忆
截图数量	10张	均匀分布
质量评分	≥25/28	优秀标准

📋 Use Quality Checklist for self-assessment

---

Key Features

✅ Content Structure

• Card-based layout (卡片式布局)
• Balanced information density (200-400字/点)
• Clear hierarchy (##/###/####)

✅ Knowledge Extraction

• 4-dimensional model: 现象+原因+方案+案例
• Core concept in one sentence (20-30字)
• 3-5 key points per card

✅ Visual Support

• 10 key screenshots
• 600px uniform width
• Precise timestamp alignment

✅ Summary Framework

• Knowledge structure tree
• Multi-dimensional practical value
• 6 actionable learning suggestions

---

Technical Implementation

Extract Subtitles

from srt_parser import parse_srt_file, get_full_transcript
segments = parse_srt_file(srt_path)
full_text = get_full_transcript(segments, include_timestamps=False)

Batch Screenshots

import subprocess
for ts in timestamps:
    cmd = ["ffmpeg", "-y", "-ss", str(ts), "-i", video_path,
           "-vframes", "1", "-q:v", "2", output_file]
    subprocess.run(cmd)

Safe JSON Output

import json
output_path.write_text(
    json.dumps(analysis, ensure_ascii=False, indent=2),
    encoding='utf-8'
)

---

Resources

Scripts

• scripts/srt_parser.py - Parse SRT subtitle files
• scripts/screenshot_tool.py - Capture video frames at specific timestamps
• scripts/report_generator.py - Generate clean-style learning notes

Reference Docs

• 📖 BEST_PRACTICES.md - 全面的最佳实践指南(535行)
• ✅ QUICK_QUALITY_CHECKLIST.md - 28项质量检查清单
• 📁 references/README.md - 参考文档导航

---

Quick Start Guide

For First-Time Users:

1. Read this SKILL.md to understand the workflow
2. Check BEST_PRACTICES.md sections 1-5
3. Review the example case: reports/2026-02-28/BV1ms4y1Y76i_*/
4. Use Quality Checklist to evaluate your output

For Experienced Users:

1. Generate notes using the skill
2. Quick check with the quality checklist
3. Reference best practices when needed
4. Optimize using technical implementation code

---

Version

Current: v1.1.0 (2026-02-28)

• ✅ Enhanced content generation guidelines
• ✅ Comprehensive best practices documentation
• ✅ 28-item quality checklist
• ✅ Real successful case examples

See CHANGELOG.md for version history.