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"

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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

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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

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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

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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'
)

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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

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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

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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.