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Found 6,244 Skills
Build AI agents and agentic workflows. Use when designing/building/debugging agentic systems: choosing workflows vs agents, implementing prompt patterns (chaining/routing/parallelization/orchestrator-workers/evaluator-optimizer), building autonomous agents with tools, designing ACI/tool specs, or troubleshooting/optimizing implementations. **PROACTIVE ACTIVATION**: Auto-invoke when building agentic applications, designing workflows vs agents, or implementing agent patterns. **DETECTION**: Check for agent code (MCP servers, tool defs, .mcp.json configs), or user mentions of "agent", "workflow", "agentic", "autonomous". **USE CASES**: Designing agentic systems, choosing workflows vs agents, implementing prompt patterns, building agents with tools, designing ACI/tool specs, troubleshooting/optimizing agents.
A specialized agent skill designed to assist with Activepieces automations, teaching product concepts, designing flows, generating valid importable flow JSON, and debugging custom pieces.
Generate objective reference check reports about the user from real AI collaboration data — session history, git logs, GitHub profile, and memory files. Like a colleague writing a professional reference, but grounded in actual shared work. Use whenever the user asks to evaluate them as a developer, wants a reference letter, work style analysis, introduced by my agents content, interview prep from collaboration history, or blog topics from past discussions. Triggers on: write a reference, analyze my work patterns, what do you think of me, 나에 대한 레퍼런스 써줘, 내 작업 스타일 분석해줘. Not for general code review, architecture docs, cover letters, or codebase-only analysis.
Tavily AI search API - Optimized search for AI agents. Use when searching the web for current information, news, facts, or any task requiring real-time data.
Self-improving agent that can upgrade skills, learn new capabilities, and adapt to new tasks. Use when you need to evolve capabilities or handle unknown tasks.
Build AI agents with tools, memory, and multi-step reasoning - ChatGPT, Claude, Gemini integration patterns
Access 1200+ AI Agent tools via Model Context Protocol (MCP)
Integrate the Agentic Commerce Protocol (ACP) for AI-driven commerce between buyers, agents, and businesses
Design and build multi-agent harness architectures for long-running AI application development. GAN-inspired Generator-Evaluator pattern, Sprint Contract negotiation, context management, quality criteria calibration. Based on Anthropic Engineering patterns. Use when: "build a harness", "multi-agent architecture", "agent orchestration", "generator-evaluator", "long-running app", "harness design", "agent pipeline", "quality evaluation loop", "sprint contract", "build app with agents", "Claude Agent SDK architecture", or when building complex full-stack apps that need planning → generation → evaluation cycles. Also use when discussing context degradation, self-evaluation bias, or assumption testing in AI workflows.
End-to-end Swiggy ordering with Prava card-token checkout. Use when the user wants an AI agent to set up Swiggy MCP, browse/search Swiggy Food/Instamart/Dineout, choose a saved delivery address, add or review Swiggy cart items, create a Prava authorization/payment session, and complete Swiggy checkout using Prava-issued tokenized card credentials. Also use when the user asks to install or configure the Swiggy MCP plus Prava payment flow for agentic purchases.
Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.
Create LangChain agents with create_agent, define tools, and use middleware for human-in-the-loop and error handling