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Found 1,134 Skills
Agno AI agent framework. Use for building multi-agent systems, AgentOS runtime, MCP server integration, and agentic AI development.
An image generation/editing Skill for GPT Image 2. It can be used in 3 environments: (A) Garden Local Mode: directly generate and save images via OpenAI-compatible APIs; (B) Host-Native Mode: treat this Skill as a prompt engineering guide, and pass the rendered prompt to the image tool built into the host Agent for image generation; (C) Advisor Mode: degrade to a high-quality prompt consultant when the host has no image tools. It covers 18 major categories and over 80 structured templates, including scenarios such as posters, UI, products, infographics, academic figures, technical architecture diagrams, comics, avatars, process boards, storyboards, IP peripherals, and editing workflows.
dontbesilent Slow is Fast. It helps entrepreneurs find methods that seem slower but deliver faster results in the long run, and build assets through friction. Trigger methods: /dbs-slowisfast, /slow-is-fast, "Is there a slower way", "Am I going too fast" Slow-is-fast diagnosis. Help entrepreneurs find seemingly slower methods that build assets through friction. Trigger: /dbs-slowisfast, "is there a slower way", "am I going too fast"
dontbesilent Goal Clarification. Audit vague goals into checkable deliverables using Wittgenstein's philosophy of language. Triggers: /dbs-goal, /goal, "help me clarify my goal", "I want to build a personal IP", "my goal is to become...", "I want to be more..." Goal clarification using Wittgenstein's philosophy of language. Audits fuzzy goals into checkable deliverables. Trigger: /dbs-goal, "help me clarify my goal", "I want to become...", "my goal is..."
Search agentmemory for past observations, sessions, and learnings about a topic. Use when the user says "recall", "remember", "what did we do", or needs context from past sessions.
Summarize the last N agent sessions for the current project, grouped by date. Use when the user asks "recap", "what have we been doing", "this week", "today", or wants a rollup of recent work.
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
Complete setup for automated agent-driven development. Define features as user stories with testable acceptance criteria, then run AI agents in a loop until all stories pass.
Google Agent Development Kit (ADK) for Python. Capabilities: AI agent building, multi-agent systems, workflow agents (sequential/parallel/loop), tool integration (Google Search, Code Execution), Vertex AI deployment, agent evaluation, human-in-the-loop flows. Actions: build, create, deploy, evaluate, orchestrate AI agents. Keywords: Google ADK, Agent Development Kit, AI agent, multi-agent system, LlmAgent, SequentialAgent, ParallelAgent, LoopAgent, tool integration, Google Search, Code Execution, Vertex AI, Cloud Run, agent evaluation, human-in-the-loop, agent orchestration, workflow agent, hierarchical coordination. Use when: building AI agents, creating multi-agent systems, implementing workflow pipelines, integrating LLM agents with tools, deploying to Vertex AI, evaluating agent performance, implementing approval flows.
Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
Guide for using Apollo MCP Server to connect AI agents with GraphQL APIs. Use this skill when: (1) setting up or configuring Apollo MCP Server, (2) defining MCP tools from GraphQL operations, (3) using introspection tools (introspect, search, validate, execute), (4) troubleshooting MCP server connectivity or tool execution issues.