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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Add new Modal webhooks for event-driven execution. Use when user asks to create a webhook, add an endpoint, or set up event triggers.
Convert formal names to casual versions for cold email personalization - first names, company names, and city names. Use when user asks to casualize names, make names friendly, or prepare lead data for emails.
Classify leads using LLM for complex distinctions like product SaaS vs agencies. Use when user asks to classify leads, filter leads by type, or categorize businesses.
Generate PandaDoc proposals from client information or sales call transcripts. Use when user asks to create a proposal, generate a quote, draft a contract, or prepare a client document.
Find viral YouTube videos from adjacent business niches to extract content patterns and hooks. Use when user asks to find content inspiration, YouTube outliers, viral video patterns, or cross-niche content ideas.
Generate weekly weather reports for Canada using Open-Meteo API (free, no API key required) and PDF generation. Use when user asks to create a weather report, generate Canada weather summary, or build weekly weather PDF.
| 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. |
| allowed-tools | Bash, Read, Write, Edit, Glob, Grep |
Query Skool community content using a RAG (Retrieval-Augmented Generation) pipeline with vector search and reranking.
./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 basepython3 ./scripts/skool_rag_prepare.py --community makerschool
Scrapes and chunks community content.
python3 ./scripts/skool_rag_index.py --input .tmp/skool_chunks.json
Creates OpenAI embeddings and stores in Pinecone.
python3 ./scripts/skool_rag_query.py --query "How do I get my first client?"
Pipeline:
PINECONE_API_KEY=your_key
OPENAI_API_KEY=your_key
COHERE_API_KEY=your_key
ANTHROPIC_API_KEY=your_key