AI Skills for OpenAI API

Enables display of HTML content on OpenClaw-connected nodes, perfect for interactive demos and visualizations.

Provides repo-specific guidance for managing cross-platform coding tasks and maintaining synchronization across multiple AI coding platforms.

Designs advanced LLM applications using LangChain and LangGraph for AI agents, memory management, and tool integration.

Guides users in selecting and optimizing embedding models for semantic search and RAG applications, enhancing model performance and quality.

Expertly builds stateful, multi-actor AI applications using LangGraph, focusing on graph construction and state management.

Generates and edits images using the OpenAI Image API for various creative projects, ensuring high-quality outputs and efficient workflows.

Enhances LLM applications with observability, tracing, and prompt management using Langfuse for improved performance and debugging.

OpenAI's Whisper model provides robust speech recognition for transcription and translation across 99 languages, ideal for audio processing.

Provides an open-source platform for LLM observability, enabling tracing, evaluation, and real-time monitoring of AI applications.

LangChain enables the development of LLM-powered applications with agents and RAG, supporting multiple providers and integrations.

Automates startup processes by orchestrating specialized agents across various domains, enabling zero human intervention from PRD to deployment.

Provides authoritative guidance on building with OpenAI products and APIs, ensuring access to the latest official documentation.

Builds AI-powered products that effectively utilize AI APIs, focusing on prompt engineering, cost management, and user experience.

LlamaIndex is a data framework for building LLM applications, enabling document ingestion, indexing, and querying for enhanced knowledge retrieval.

Facilitates code analysis, refactoring, and automated editing using OpenAI Codex CLI for enhanced software engineering workflows.

Provides a platform for monitoring and debugging LLM applications, enabling systematic evaluation and tracing of model outputs.

Enables the creation of complex AI systems using declarative programming and automatic prompt optimization with DSPy from Stanford NLP.

Facilitates structured data extraction from LLM responses using Pydantic validation and supports multiple LLM providers.

Expertly builds real-time voice AI applications using various APIs for transcription, synthesis, and low-latency voice experiences.

Enables the development of voice agents for natural AI interactions, focusing on low-latency conversation flow and emotional nuance.