Skill UI

A comprehensive guide and implementation patterns for constructing sophisticated autonomous AI agents. It details the core 'Observe-Think-Decide-Act' agent loop, multi-model orchestration strategies, and robust tool design principles (like file I/O and function calling). Ideal for building complex, highly capable agents that require advanced planning, reasoning, and external tool interaction.

A comprehensive guide and implementation patterns for constructing sophisticated autonomous AI agents. It details the core 'Observe-Think-Decide-Act' agent loop, multi-model orchestration strategies, and robust tool design principles (like file I/O and function calling). Ideal for building complex, highly capable agents that require advanced planning, reasoning, and external tool interaction.

A comprehensive workflow designed for clinical professionals to search, retrieve, and synthesize medical evidence. It allows users to query clinical literature with specialized filters (e.g., specialty, evidence level), check potential drug interactions against patient profiles, and look up the latest specialty guidelines from major health organizations (like ACC/AHA, NICE). The output provides structured citations, confidence scores, and evidence grading to support informed clinical decision-making.

A comprehensive collection of battle-tested, structured prompts designed to elevate interactions with Large Language Models (LLMs). Covers advanced use cases including expert coding reviews, system architecture design, performance analysis, technical writing, and creative brainstorming, ensuring high-quality, actionable AI output.

This skill provides implementation patterns for integrating Retell AI's advanced voice agent and telephony features. It is designed for developers building automated systems that require handling complex voice interactions, making scheduled phone calls, or managing conversational AI workflows within a CI/CD or automation environment.

This skill provides implementation patterns for policy guardrails when utilizing Retell AI for voice agents and phone call automation. It is designed to help ensure that AI interactions in a telephony context adhere strictly to predefined business rules and safety policies, preventing unauthorized actions or deviations from the intended conversational flow.

Provides implementation patterns and reference architecture for building advanced AI voice agents and automating complex phone call workflows using the Retell AI SDK. This skill is designed for developers requiring robust voice interaction, real-time telephony integration, and comprehensive conversational AI capabilities in their applications.

A conversational onboarding skill designed to deeply understand the user and define the core identity of an AI partner. Through adaptive, multi-phase dialogue, it extracts key behavioral traits, motivations, and relationship dynamics, culminating in the generation of a comprehensive SOUL.md file that governs the AI's personality and interaction style. Use this skill when setting up, personalizing, or updating your AI agent.

This skill provides comprehensive interaction with the DeerFlow AI agent platform via its robust HTTP API. It is designed for managing complex, multi-step AI workflows, including deep academic research, code execution, web browsing, and file analysis. Users can initiate and continue conversation threads, manage agent memory, upload documents (PDF, etc.), and delegate complex research tasks to the autonomous agent system.

Evolver is a comprehensive self-evolution engine designed for autonomous AI agents. It analyzes complex runtime history, identifies performance bottlenecks, and autonomously generates and applies improvements. Communication is architecturally confined to a local Proxy Mailbox, ensuring secure and reliable interaction with the EvoMap Hub for asset management and task acquisition. Ideal for developing highly adaptive, continuously improving intelligent systems.

A comprehensive coaching layer designed to elevate users into power users of AI models. Upon first activation, it provides a structured glossary of high-impact techniques tailored to user needs. In subsequent turns, it actively monitors prompts, surfacing single, actionable tips to optimize interactions and maximize the AI's capabilities, ensuring continuous, guided learning.

SkillOpt-Sleep provides a mechanism for autonomous agent self-improvement. By simulating a 'sleep' or 'dream' cycle, it harvests past interaction sessions, mines recurring tasks, and replays them through the designated backend. It consolidates learned knowledge into persistent memory and skills, ensuring that only strictly validated improvements are adopted via a held-out gate, simulating long-term competence gain from short-term experience. Ideal for scheduled, background optimization.