Introduction
In the era of AI-assisted coding, context is king. Large Language Models (LLMs) like Claude excel when they have access to specific, up-to-date documentation and codebases. However, manually feeding this information into an AI context window is often tedious and error-prone.
Enter Skill Seeker, an open-source tool designed to bridge the gap between external knowledge and your AI assistant. By converting documentation websites, GitHub repositories, and PDFs into optimized "skills," Skill Seeker empowers developers to build comprehensive knowledge bases for Claude efficiently.
What is Skill Seeker?
Skill Seeker is a versatile utility created by Yusuf Karaaslan that automates the process of gathering and structuralizing technical information. It serves as a unified multi-source scraper that can ingest content from various formats and output it in a way that is easily consumable by AI models, particularly Claude.
Whether you need to let your AI read the latest framework documentation, understand a specific GitHub repository, or analyze a technical PDF, Skill Seeker handles the heavy lifting of extraction, cleaning, and formatting.
Key Features
Skill Seeker has evolved rapidly, with its recent v2.0.0 release bringing significant enhancements. Here are some of its standout capabilities:
- 🌐 Documentation Scraping: detailed scraping of documentation websites to capture guides, API references, and tutorials.
- 🐙 GitHub Repository Scraping: A major addition in v2.0.0, this allows users to pull entire repositories, making it easier for Claude to understand codebase structures and logic.
- 📄 PDF Support: Introduced in v1.2.0, this feature unlocks knowledge locked in PDF files, such as research papers or legacy manuals.
- 🔄 Unified Multi-Source Scraping: The new v2.0.0 architecture supports scraping from multiple sources simultaneously, streamlining the knowledge acquisition workflow.
- 🤖 AI & Enhancement: The tool uses AI to enhance the structure and quality of the extracted data, ensuring it provides the best possible context.
- ⚡ Performance & Scale: Built for speed, it includes features like fast page estimation and async modes for 2-3x faster scraping.
- ✅ Automatic Conflict Detection: Helps manage data integrity when merging information from different sources.
How It Works
Skill Seeker operates by taking a target (URL, repo link, or file path) and processing it through its extraction engine.
- Input: You provide the source, such as a documentation URL or a GitHub repository link.
- Scraping & Extraction: The tool crawls the source, respecting site maps and repository structures. It cleans the HTML or extracts text from PDFs/code files.
- Optimization: It applies AI enhancements to structure the data, generating a
SKILL.mdfile or similar artifact that summarizes the content effectively. - Output: The final output is a structured set of files or a single knowledge referencing file that you can feed into Claude.
Getting Started
Using Skill Seeker is straightforward, with multiple installation options available depending on your workflow.
Installation
You can install it directly from PyPI (recommended):
pip install skill-seeker
Or using uv, a modern Python tool:
uv tool install skill-seeker
Basic Usage
To scrape a documentation site:
skill-seeker https://docs.example.com
To scrape a GitHub repository:
skill-seeker https://github.com/username/repo
For a comprehensive setup, you can run it in interactive mode to configure specific scraping parameters:
skill-seeker --interactive
Why Use Skill Seeker?
For developers working with Claude, Skill Seeker solves the "context window" problem by pre-processing information. Instead of pasting raw text or struggling with token limits, you get a curated, dense representation of the external knowledge.
- For Libraries & Frameworks: Quickly generate a "skill" for a new library that Claude hasn't been trained on yet.
- For Legacy Code: Ingest old documentation (PDFs) and internal repos to help Claude refactor or explain legacy systems.
- For Productive Workflows: With the MCP server integration, you can use Skill Seeker directly within environments that support the Model Context Protocol, making the workflow seamless.
Conclusion
Skill Seeker represents a significant step forward in making AI assistants more autonomous and knowledgeable. By automating the ingestion of technical documentation and code, it allows developers to focus on higher-level problem solving rather than data entry.
If you are looking to supercharge your experience with Claude, giving Skill Seeker a try is a must. Its active development and growing feature set make it a valuable tool in any AI engineer's toolkit.