Can it extract audio or subtitles?

This specific skill focuses on metadata and video downloads. For audio (MP3) and subtitles (SRT), it is designed to be used as an upstream process for the rip-video skill.

How does it track which videos have been downloaded?

The skill reads the checkbox state in your todolist.md file and updates [ ] to [x] only after the metadata, video, or cover has been successfully saved.

What happens if an MCP service rate limit is reached?

The skill detects rate limits, displays the remaining quota to the user, and allows for retries or graceful skipping of tasks to avoid service lockout.

What is required to run the parse-video skill?

You must have a configured MCP parse-video service running and a todolist.md file in your directory containing the target video URLs and paths.

Automated Video Parser & Downloader

Name: Automated Video Parser & Downloader
Author: mojito-llm

bymojito-llm

Web Scraping & Data Collection

Automates video metadata extraction and file downloading by processing task lists through MCP services.

About

The parse-video skill is a specialized workflow automation tool for Claude Code designed to handle bulk media acquisition. It operates by reading a todolist.md file, extracting video identifiers and URLs, and then orchestrating MCP services to fetch metadata and download media assets. The skill manages the entire lifecycle of a video task—from saving structured JSON metadata and MP4 files to updating task checkboxes in markdown—making it an ideal solution for developers building automated content pipelines or media archival systems.

Key Features

0 GitHub stars
Built-in error reporting and rate-limit monitoring
Automated task processing from todolist.md files
High-quality video and cover image downloading
Automatic markdown status updates for progress tracking
Structured metadata extraction and JSON generation

Use Cases

Building local media libraries with structured metadata
Automated ingestion of video assets into CMS pipelines
Bulk archival of social media video content for research

About

Key Features

0 GitHub stars
Built-in error reporting and rate-limit monitoring
Automated task processing from todolist.md files
High-quality video and cover image downloading
Automatic markdown status updates for progress tracking
Structured metadata extraction and JSON generation

Use Cases

Building local media libraries with structured metadata
Automated ingestion of video assets into CMS pipelines
Bulk archival of social media video content for research