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Found 67 Skills
Use the unified Opper SDKs (`opperai` package for both Python and TypeScript, with built-in agent support) for AI task completion, structured output with Pydantic / Zod / JSON Schema, knowledge base semantic search, streaming, tracing, tool use, and multi-agent composition. Use this skill whenever the user is writing Python or TypeScript code that imports `opperai`, builds an Opper agent, or asks how to do anything Opper-related in code — even if they don't explicitly name the SDK. Both languages live in one repo with parallel numbered examples; agents are part of the SDK, not a separate package.
Use when building or editing any AI feature in n8n: AI Agents, Text Classifier, Information Extractor, Sentiment Analysis, Summarization Chain, Basic LLM Chain, embeddings, vector stores, single one-shot LLM calls, or AI media generation (image / audio / video) via the native LangChain provider nodes. Triggers on any `@n8n/n8n-nodes-langchain.*` node, "agent", "chat assistant", "LLM with tools", "tool calling", "fromAi", "system prompt", "memory window", "structured output", "outputParser", "function calling", "RAG", "vector store", "embeddings", "classify with AI", "extract fields with LLM", "sentiment analysis", "summarize with LLM", "single LLM call", chat triggers with files, AI image / video / audio generation, or any multi-turn or one-shot LLM behavior.
Use this skill when crafting, iterating, or optimizing prompts for LLMs including zero-shot, few-shot, chain-of-thought, role prompting, structured output, and prompt chaining. Not for fine-tuning or training models. Not for evaluating model quality across benchmarks.
Call Exa Search directly with cURL or raw HTTP. Use when an agent needs Exa semantic web retrieval from POST /search without an SDK, including ranked results, domain or category filters, freshness-aware result content, highlights or text extraction, structured output, or streaming search responses.
LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing
Write effective prompts for LLMs — structure, few-shot examples, chain-of-thought, system prompts, and output parsing.
This skill is used when users explicitly request to "extract review themes from files/images/webpages/descriptions", "generate structured output of theme + keywords + core questions", or request to use the old name "get-review-theme" skill. It supports multiple input sources including files (PDF/Word/Markdown/Tex), folders, images, natural language descriptions, and webpage URLs. It automatically identifies the input type, extracts content, and generates structured output that can be directly used for research-literature-review and other literature review skills.