bilibili-video-analyzer

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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.

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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  --quality 1080p --output 

Step 4: Transcribe Audio

bili-dl transcribe  --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  -i  -vframes 1 -q:v 2 

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.

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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