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):

📋 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.