Summary

This project provides a suite of productivity skills for Claude Code, designed to streamline the process of extracting content from various sources like articles, PDFs, and YouTube videos. It targets users aiming to accelerate learning and workflow by automating content processing and generating actionable implementation plans, enabling a "learn by doing" approach.

How It Works

The core is the "Tapestry" master skill, which orchestrates a unified workflow. Upon receiving a URL, it automatically detects the content type, extracts clean, relevant information, and then leverages the "Ship-Learn-Next" framework to create a structured, actionable plan. Sub-skills include a YouTube Transcript Downloader using yt-dlp (with Whisper fallback) and an Article Extractor employing Mozilla Readability or trafilatura, both focused on delivering clean text devoid of clutter.

Quick Start & Requirements

Installation is straightforward via a provided install.sh script after cloning the repository, or manually by copying skill directories. Key dependencies include yt-dlp (auto-installed), optional openai-whisper for transcription, and reader-cli or trafilatura for article extraction. Users require a Claude Code environment. Setup is minimal, primarily involving script execution or file copying.

Highlighted Details

• Unified "Tapestry" skill automates content extraction and action plan generation from a single command.
• Intelligent content type detection (YouTube, articles, PDFs).
• "Ship-Learn-Next" framework transforms passive learning into concrete, iterative action plans (5 reps).
• Emphasis on practical application: "DOING over studying" and "100 reps beats 100 hours of study."
• Automatic deduplication of YouTube transcripts and cleanup of temporary files.

Maintenance & Community

Contributions are welcomed via standard GitHub pull request workflows. Users experiencing issues are directed to open GitHub issues for support. No specific community channels (like Discord or Slack) or active contributor/sponsorship details are listed in the README.

Licensing & Compatibility

The project is released under the permissive MIT License, generally allowing for commercial use and integration into closed-source projects without significant restrictions.

Limitations & Caveats

Fallback mechanisms for transcription and article extraction may offer reduced accuracy compared to primary methods. The install.sh script's success depends on the user's operating system environment. The repository clone URL in the README uses a placeholder (yourusername).