This n8n workflow automates the process of generating and updating YouTube video chapters by extracting captions, analyzing transcripts, and editing video descriptions. It streamlines content management for creators aiming to improve video navigation and viewer engagement.

The workflow begins with a manual trigger, allowing users to start the process on demand. It sets a specific video ID and fetches detailed video metadata from YouTube, including the title and description. Concurrently, it retrieves closed captions (subtitles) using the YouTube API, then extracts the caption text for processing.

Next, the caption text is analyzed with a language model (Google Gemini) to classify sections of the video into chapters such as Introduction, Topics, and Conclusion, based on timestamps and transcript data. These chapters are then formatted and appended to the video’s description, providing clear navigation markers for viewers.

Finally, the workflow updates the YouTube video description with the new chapter information, improving video usability and indexing. This workflow is particularly useful for content creators who want to automate chapter creation, enhance video SEO, and ensure viewers can easily navigate lengthy videos.

Overall, this automation makes video management more efficient and helps maintain an organized, viewer-friendly channel.

Node Count	11 – 20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.chainLlm, @n8n/n8n-nodes-langchain.lmChatGoogleGemini, @n8n/n8n-nodes-langchain.outputParserStructured, extractFromFile, httpRequest, manualTrigger, set, stickyNote, youTube