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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
Use this skill for requests related to LangGraph in order to fetch relevant documentation to provide accurate, up-to-date guidance.
Add new Modal webhooks for event-driven execution. Use when user asks to create a webhook, add an endpoint, or set up event triggers.
MUST READ before writing or modifying ADK agent code. ADK API quick reference for Python — agent types, tool definitions, orchestration patterns, callbacks, and state management. Includes an index of all ADK documentation pages. Do NOT use for creating new projects (use adk-scaffold).
MUST READ before deploying any ADK agent. ADK deployment guide — Agent Engine, Cloud Run, GKE, CI/CD pipelines, secrets, observability, and production workflows. Use when deploying agents to Google Cloud or troubleshooting deployments. Do NOT use for API code patterns (use adk-cheatsheet), evaluation (use adk-eval-guide), or project scaffolding (use adk-scaffold).
MUST READ before setting up observability for ADK agents or when analyzing production traffic, debugging agent behavior, or improving agent performance. ADK observability guide — Cloud Trace, prompt-response logging, BigQuery Agent Analytics, third-party integrations, and troubleshooting. Use when configuring monitoring, tracing, or logging for agents, or when understanding how a deployed agent handles real traffic.
MUST READ before creating or enhancing any ADK agent project. Use when the user wants to build a new agent (e.g. "build me a search agent") or enhance an existing project (e.g. "add CI/CD to my project", "add 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. |
| 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