skool-rag

name	skool-rag
description	Query Skool community content using RAG pipeline with vector search. Use when user asks to search Skool knowledge, find community answers, or query Skool content.

Skool RAG Pipeline

Goal

Query Skool community content using a RAG (Retrieval-Augmented Generation) pipeline with vector search and reranking.

Scripts

• ./scripts/skool_rag_prepare.py - Prepare content for indexing
• ./scripts/skool_rag_index.py - Index content in Pinecone
• ./scripts/skool_rag_query.py - Query the knowledge base

Pipeline

1. Prepare Content

python3 ./scripts/skool_rag_prepare.py --community makerschool

Scrapes and chunks community content.

2. Index in Pinecone

python3 ./scripts/skool_rag_index.py --input .tmp/skool_chunks.json

Creates OpenAI embeddings and stores in Pinecone.

3. Query

python3 ./scripts/skool_rag_query.py --query "How do I get my first client?"

Pipeline:

1. OpenAI embeddings for query
2. Pinecone vector search
3. Cohere reranking
4. Claude response generation

Environment

PINECONE_API_KEY=your_key
OPENAI_API_KEY=your_key
COHERE_API_KEY=your_key
ANTHROPIC_API_KEY=your_key

---

Schema

Inputs

Name	Type	Required	Description
query	string	Yes	Natural language question to search for
community	string	No	Community slug to index (default: makerschool)

Outputs

Name	Type	Description
answer	string	AI-generated answer with source references

Credentials

Name	Source
PINECONE_API_KEY	.env
OPENAI_API_KEY	.env
COHERE_API_KEY	.env
ANTHROPIC_API_KEY	.env

Composable With

Skills that chain well with this one: skool-monitor

Cost

Pinecone + OpenAI embeddings + Cohere reranking + Claude