multi-llm-advisor
Fetches expert perspectives from OpenAI Codex and Google Gemini for architecture, code reviews, and debugging, with transparent LLM synthesis.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
143 skills found
Fetches expert perspectives from OpenAI Codex and Google Gemini for architecture, code reviews, and debugging, with transparent LLM synthesis.
Full-stack application orchestrator that analyzes natural language requests to determine tech stacks, scaffold projects, and coordinate specialized development agents.
A wise conductor of expert agents. It helps you achieve goals by summoning, orchestrating, and creating specialized AI experts. Features intellectual humility, multi-agent debate, and self-learning pattern capture.
A specialized decision-making agent for complex architectural choices, task planning, and error resolution within the orchestration system.
Search, discover, and refine AI prompts using the prompts.chat library. Access thousands of community-curated prompts for ChatGPT, Claude, and other AI models.
Advanced context engineering system for orchestrating AI agents, memory management, and token optimization to improve long-term persistence and project intelligence.
An AI-powered sales assistant that transforms business scenarios into optimized prompts, automatically generating high-quality emails, proposals, and analysis reports without requiring prompt engineering skills.
Intelligent pattern selection for Fabric CLI, automatically choosing from 242+ specialized prompts for threat modeling, data analysis, summarization, and content creation.
Build AI agents with the OpenAI Agents SDK for Python. Supports multi-agent handoffs, function tools, stateful sessions, streaming, and Azure OpenAI integration via LiteLLM.
Google Gemini Image Generation API interface for text-to-image, editing, style templates, and automated retry workflows.
Expert skill for implementing the Gemini Interactions API. Use for stateful multi-turn chat, background Deep Research agent tasks, function calling, structured outputs, and modern Python/TypeScript SDK integration.
Generate a structured academic paper outline from research narrative, experiment data, and review conclusions.