consolidate-transcripts

Name: consolidate-transcripts
Availability: InStock
Author: dparedesi

Merge YouTube channel transcripts into one markdown file within token limits.

7|1|Updated Dec 6, 2025

Search Tags:#youtube #data processing #consolidation #transcripts #llm context #token limit

Ready to run

npx skills add https://github.com/dparedesi/YTScribe --skill consolidate-transcripts

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: consolidate-transcripts
Source: https://github.com/dparedesi/YTScribe/tree/main/.agent/skills/consolidate-transcripts
Command: npx skills add https://github.com/dparedesi/YTScribe --skill consolidate-transcripts

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tiktoken, and includes scripts (resource) components.

What problem does it solve?

This Skill addresses the challenge of LLM context window limitations by consolidating multiple YouTube transcripts into a single, manageable file, ensuring all relevant information fits within token limits for analysis or prompting.

Core Features & Use Cases

Transcript Merging: Combines transcripts from a specified YouTube channel into one markdown file.
Token Limit Management: Intelligently includes transcripts up to a configurable token limit (defaulting to 800K for Claude).
Sorted Output: Organizes transcripts by date, newest first, for chronological analysis.
Use Case: Prepare all transcripts from a specific YouTube channel for a large language model like Claude, ensuring you don't exceed its context window while retaining the most recent content.

Quick Start

Consolidate all transcripts for the 'library-of-minds' channel into a single file.

Frequently Asked Questions about consolidate-transcripts

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I consolidate YouTube transcripts for LLM analysis without exceeding token limits?▼

Consolidating YouTube transcripts for LLM analysis requires merging individual video files into a single markdown document while tracking token counts. This process ensures all relevant text fits within model context windows by stopping inclusion once a maximum token threshold is reached.

What is the best way to prepare multiple YouTube video transcripts for Claude's context window?▼

Preparing multiple YouTube video transcripts for Claude involves combining them into one file sorted by date. Using tiktoken for accurate GPT-4 and Claude token counting, the process caps total tokens at a configurable limit like 800K to prevent context window overflow.

How do I merge YouTube transcripts sorted by date into a single markdown file?▼

Merging YouTube transcripts sorted by date into a single markdown file involves reading transcripts from a specified channel directory, ordering them newest first, and concatenating the text. This chronological organization ensures the most recent content is prioritized for analysis.

Does tiktoken work for counting tokens when consolidating transcripts for Claude models?▼

Tiktoken works for counting tokens when consolidating transcripts for Claude models. It provides accurate token estimation compatible with GPT-4 and Claude, allowing the consolidation process to intelligently include transcripts up to a strict token limit without exceeding model capabilities.

Why does my LLM context window overflow when analyzing YouTube channel transcripts?▼

LLM context windows overflow when analyzing YouTube channel transcripts because raw combined text often exceeds model token capacities. Consolidating transcripts with intelligent token limit management and tiktoken counting prevents this by truncating the output before it breaches the maximum allowed context.

Can I limit consolidated YouTube transcripts to a specific token count for LLM prompting?▼

You can limit consolidated YouTube transcripts to a specific token count for LLM prompting by setting a configurable maximum token threshold. The consolidation process evaluates each transcript's token size and stops adding content once the desired limit is reached.