Can it handle videos without manual captions?

Yes, the skill is configured to prefer high-quality manual subtitles but will fall back to auto-generated transcripts if they are available.

How does it process long-form videos or technical talks?

The skill uses intelligent chunking for videos over 45 minutes, summarizing logical segments to maintain context while staying within AI token limits.

Does it provide timestamps for its findings?

Yes, the analysis format specifically includes timestamps for key insights so you can easily jump to the relevant part of the video.

What prerequisites are needed for this skill?

You must have yt-dlp installed on your system via pip (pip install yt-dlp) or Homebrew (brew install yt-dlp) to extract transcripts.

YouTube Transcript Analyzer

Name: YouTube Transcript Analyzer
Author: TechNickAI

byTechNickAI

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Learning & Documentation

Analyzes YouTube video transcripts to extract technical insights and relate them directly to your codebase.

About

This skill leverages yt-dlp to reliably extract and process YouTube transcripts, enabling developers to learn from video tutorials, conference talks, and technical presentations without manual watching. It features intelligent chunking for long-form content, handles multiple languages, and provides a structured analysis that correlates video insights directly with your project's current implementation and architectural goals, turning hours of video into actionable development tasks.

Key Features

Context-aware analysis mapping video concepts to local codebases
Intelligent chunking for processing long-form content exceeding 45 minutes
Timestamp-referenced extraction for quick navigation to relevant video segments
10 GitHub stars
Automated transcript extraction using yt-dlp with metadata retrieval
Structured summaries including key insights and actionable recommendations

Use Cases

Comparing a library tutorial video with your existing implementation for optimization
Extracting design patterns from technical conference talks to apply to your current project
Performing rapid research on technical concepts across multiple long-form presentations

About

Key Features

Context-aware analysis mapping video concepts to local codebases
Intelligent chunking for processing long-form content exceeding 45 minutes
Timestamp-referenced extraction for quick navigation to relevant video segments
10 GitHub stars
Automated transcript extraction using yt-dlp with metadata retrieval
Structured summaries including key insights and actionable recommendations

Use Cases

Comparing a library tutorial video with your existing implementation for optimization
Extracting design patterns from technical conference talks to apply to your current project
Performing rapid research on technical concepts across multiple long-form presentations