subagent-prompting
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denario
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
elevenlabs-agents
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langchain-architecture
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
sf-ai-agentforce-testing
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emerging-tech
Emerging technologies (prompt engineering, AI agents, red teaming) and leadership roles (product manager, engineering manager, DevRel).
agent-design
AI agent design and tool-use prompting patterns
aide
AI agent integration with Jira and Azure DevOps for ticket-driven development and PR review workflows
langchain-architecture
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
executing-ai-development-workflow
Execute a comprehensive AI-driven development workflow with planning, implementation, multi-layer review (Sub-agents + /review + CodeRabbit CLI), automated fixes, and PR creation. Use when implementing new features, performing large refactorings, developing security-critical features, or when the user mentions "AI開発ワークフロー", "AI development workflow", or "計画的に実装".
building-multiagent-systems
This skill should be used when designing or implementing systems with multiple AI agents that coordinate to accomplish tasks. Triggers on "multi-agent", "orchestrator", "sub-agent", "coordination", "delegation", "parallel agents", "sequential pipeline", "fan-out", "map-reduce", "spawn agents", "agent hierarchy".
33GOD System Expert
Deep knowledge expert for the 33GOD agentic pipeline system, understands component relationships and suggests feature implementations based on actual codebase state
agno
Agno AI agent framework. Use for building multi-agent systems, AgentOS runtime, MCP server integration, and agentic AI development.
Convex Agents Streaming
Streams agent responses in real-time to clients without blocking. Use this for responsive UIs, long-running generations, and asynchronous streaming to multiple clients.
Convex Agents Fundamentals
Sets up and configures Convex agents for chat-based AI interactions. Use this when initializing agent instances, creating conversation threads, and generating basic text or structured responses from LLMs. Essential foundation for any Convex agent implementation.
argentic-framework-development
Expert knowledge for building AI agents with Argentic - a Python microframework for async MQTT-based agents with multi-LLM support, custom tools, and multi-agent orchestration
building-ai-agent-on-cloudflare
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deepagents-code-review
Reviews Deep Agents code for bugs, anti-patterns, and improvements. Use when reviewing code that uses create_deep_agent, backends, subagents, middleware, or human-in-the-loop patterns. Catches common configuration and usage mistakes.
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