Skill Seekers v3.0.0: The Universal Data Preprocessor for AI Systems

yusyus — Sun, 08 Feb 2026 22:30:56 +0000

🚀 One command converts any documentation into structured knowledge for any AI system.

TL;DR

🎯 16 output formats (was 4 in v2.x)
🛠️ 26 MCP tools for AI agents
✅ 1,852 tests passing
☁️ Cloud storage support (S3, GCS, Azure)
🔄 CI/CD ready with GitHub Action

pip install skill-seekers
skill-seekers scrape --config react.json

The Problem We're All Solving

Raise your hand if you've written this code before:

# The custom scraper we all write
import requests
from bs4 import BeautifulSoup

def scrape_docs(url):
    # Handle pagination
    # Extract clean text
    # Preserve code blocks
    # Add metadata
    # Chunk properly
    # Format for vector DB
    # ... 200 lines later
    pass

Every AI project needs documentation preprocessing.

RAG pipelines: "Scrape these docs, chunk them, embed them..."
AI coding tools: "I wish Cursor knew this framework..."
Claude skills: "Convert this documentation into a skill"

We all rebuild the same infrastructure. Stop rebuilding. Start using.

Meet Skill Seekers v3.0.0

One command → Any format → Production-ready

For RAG Pipelines

# LangChain Documents
skill-seekers scrape --format langchain --config react.json

# LlamaIndex TextNodes
skill-seekers scrape --format llama-index --config vue.json

# Pinecone-ready markdown
skill-seekers scrape --target markdown --config django.json

Then in Python:

from skill_seekers.cli.adaptors import get_adaptor

adaptor = get_adaptor('langchain')
documents = adaptor.load_documents("output/react/")

# Now use with any vector store
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings

vectorstore = Chroma.from_documents(
    documents,
    OpenAIEmbeddings()
)

For AI Coding Assistants

# Give Cursor framework knowledge
skill-seekers scrape --target claude --config react.json
cp output/react-claude/.cursorrules ./

Result: Cursor now knows React hooks, patterns, and best practices from the actual documentation.

For Claude AI

# Complete workflow: fetch → scrape → enhance → package → upload
skill-seekers install --config react.json

What's New in v3.0.0

16 Platform Adaptors

Category	Platforms	Use Case
RAG/Vectors	LangChain, LlamaIndex, Chroma, FAISS, Haystack, Qdrant, Weaviate	Build production RAG pipelines
AI Platforms	Claude, Gemini, OpenAI	Create AI skills
AI Coding	Cursor, Windsurf, Cline, Continue.dev	Framework-specific AI assistance
Generic	Markdown	Any vector database

26 MCP Tools

Your AI agent can now prepare its own knowledge:

🔧 Config: generate_config, list_configs, validate_config
🌐 Scraping: scrape_docs, scrape_github, scrape_pdf, scrape_codebase
📦 Packaging: package_skill, upload_skill, enhance_skill, install_skill
☁️ Cloud: upload to S3, GCS, Azure
🔗 Sources: fetch_config, add_config_source
✂️ Splitting: split_config, generate_router
🗄️ Vector DBs: export_to_weaviate, export_to_chroma, export_to_faiss, export_to_qdrant

Cloud Storage

# Upload to AWS S3
skill-seekers cloud upload output/ --provider s3 --bucket my-bucket

# Or Google Cloud Storage
skill-seekers cloud upload output/ --provider gcs --bucket my-bucket

# Or Azure Blob Storage
skill-seekers cloud upload output/ --provider azure --container my-container

CI/CD Ready

# .github/workflows/update-docs.yml
- uses: skill-seekers/action@v1
  with:
    config: configs/react.json
    format: langchain

Auto-update your AI knowledge when documentation changes.

Why This Matters

Before Skill Seekers

Week 1: Build custom scraper
Week 2: Handle edge cases
Week 3: Format for your tool
Week 4: Maintain and debug

After Skill Seekers

15 minutes: Install and run
Done: Production-ready output

Real Example: React + LangChain + Chroma

# 1. Install
pip install skill-seekers langchain-chroma langchain-openai

# 2. Scrape React docs
skill-seekers scrape --format langchain --config configs/react.json

# 3. Create RAG pipeline

from skill_seekers.cli.adaptors import get_adaptor
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain.chains import RetrievalQA

# Load documents
adaptor = get_adaptor('langchain')
documents = adaptor.load_documents("output/react/")

# Create vector store
vectorstore = Chroma.from_documents(
    documents,
    OpenAIEmbeddings()
)

# Query
qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(),
    retriever=vectorstore.as_retriever()
)

result = qa_chain.invoke({"query": "What are React Hooks?"})
print(result["result"])

That's it. 15 minutes from docs to working RAG pipeline.

Production Ready

✅ 1,852 tests across 100 test files
✅ 58,512 lines of Python code
✅ CI/CD on every commit
✅ Docker images available
✅ Multi-platform (Ubuntu, macOS)
✅ Python 3.10-3.13 tested

Get Started

# Install
pip install skill-seekers

# Try an example
skill-seekers scrape --config configs/react.json

# Or create your own config
skill-seekers config --wizard

What's Next?

⭐ Star us on GitHub if you hate writing scrapers
🐛 Report issues (1,852 tests but bugs happen)
💡 Suggest features (we're building in public)
🚀 Share your use case

Skill Seekers v3.0.0 was released on February 10, 2026. This is our biggest release yet - transforming from a Claude skill generator into a universal documentation preprocessor for the entire AI ecosystem.

Forem: yusyus