Skill UI

RecallMax dramatically enhances AI agent memory capabilities by cleanly injecting massive external contexts (500K-1M tokens) without hallucination drift. It automatically summarizes lengthy conversations, preserving tone, intent, and key facts. Furthermore, it compresses multi-turn interaction histories into high-density token sequences, ensuring agents maintain perfect recall across complex and long-running sessions.

This guide details how to build an MCP app, which is an MCP server enhanced to serve interactive UI resources. It allows developers to render complex widgets—such as forms, pickers, dashboards, and confirmation dialogs—directly inline within a chat conversation. Learn about the architecture, host specifics, and when to use a widget versus standard text output.

Defines the Lark/Feishu channel output rules so automated or human replies stay short, conversational, and mindful of Lark Markdown peculiarities when responding to coworker conversations.

Guides calling Feishu IM APIs to retrieve chat histories, thread replies, cross-chat searches, and media/file downloads while handling pagination, time filters, and permission constraints so agents can answer questions about past conversations.

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.

Describes how to use the MemOS local memory toolkit to recall past conversations, search shared histories, and choose the right memory tool when automatic recall is insufficient.

This guide details the OpenViking long-term memory plugin, which enhances conversational AI by automatically capturing key facts and preferences from past interactions. It utilizes auto-capture modes (semantic or keyword) to store memories and automatically recalls relevant context before generating a response, ensuring continuity and personalization across long conversations. Provides tools for retrieval, manual storage, and memory deletion.

This skill allows agents to ingest external resources (local files, URLs, repositories) using `ov add-resource`, and to create long-lasting searchable memories from conversations using `ov add-memory`. It is essential for building a comprehensive, persistent knowledge base for the OpenViking context database, ensuring that learned insights and external data remain available across sessions and interactions. Use when external knowledge injection or context persistence is required.

This skill transforms written content, such as articles and reports, into engaging, two-host conversational podcast audio. It first structures the content into a detailed JSON script (dialogue format) and then uses advanced Text-to-Speech synthesis to generate the final MP3 file. Supports both English and Chinese languages, making content accessible for audio-only listening.

A comprehensive toolkit to automate core Slack workspace operations. Users can manage messaging (sending, updating, replying), perform advanced message searches, list and manage channels and users, and handle reactions and threaded conversations. Ideal for integrating Slack into automated business workflows.

This skill provides comprehensive tools to download, export, and deeply inspect existing Arize traces and spans. It is essential for understanding the full execution flow of complex LLM applications, debugging runtime errors, and performing root-cause analysis. Users can retrieve detailed data filtered by Trace ID, Span ID, or Session ID, enabling thorough analysis of conversational or multi-step agent behaviors.