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.
license: MIT
---

# 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
```bash
python3 ./scripts/skool_rag_prepare.py --community makerschool
```
Scrapes and chunks community content.

### 2. Index in Pinecone
```bash
python3 ./scripts/skool_rag_index.py --input .tmp/skool_chunks.json
```
Creates OpenAI embeddings and stores in Pinecone.

### 3. Query
```bash
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