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Found 409 Skills
OpenAI Agents SDK for JavaScript/TypeScript (text + voice agents). Use for multi-agent workflows, tools, guardrails, or encountering Zod errors, MCP failures, infinite loops, tool call issues.
Use when creating or improving golden datasets for AI evaluation. Defines quality criteria, curation workflows, and multi-agent analysis patterns for test data.
Use when designing multi-agent systems, implementing supervisor patterns, coordinating multiple agents, or asking about "multi-agent", "supervisor pattern", "swarm", "agent handoffs", "orchestration", "parallel agents"
Iterative codebase quality audit with multi-agent validation and escalating-depth SEEK/VALIDATE/FIX/RECURSE cycle. Use for quality audit, code audit, codebase review, technical debt audit, refactoring opportunities, module quality check, or architecture review.
Эксперт по оркестрации AI агентов. Используй для multi-agent systems, agent coordination, task delegation и agent workflows.
Perform exhaustive code reviews using multi-agent analysis, ultra-thinking, and worktrees
Design and enforce AI-friendly verification for a GRACE project. Use when modules need stronger automated tests, traceable logs, execution-trace checks, or verification that is robust enough for autonomous and multi-agent workflows.
Agent spawning, lifecycle management, and coordination patterns. Manages 60+ agent types with specialized capabilities. Use when: spawning agents, coordinating multi-agent tasks, managing agent pools. Skip when: single-agent work, no coordination needed.
Native web workspace for Hermes Agent with chat, terminal, memory, skills, inspector, and multi-agent orchestration
Guides engineering of multi-agent systems—agent roles and specialization, orchestration topologies (supervisor, peer-to-peer, hierarchical, blackboard), task decomposition and routing, inter-agent messaging (A2A-style patterns), shared vs partitioned state, fan-out/fan-in and DAG workflows, synchronization and consensus, conflict resolution, fault tolerance and retries across agents, cost/latency/token budgets, cross-agent observability, testing multi-agent flows, and deployment (queues, durable workflows). Framework-agnostic; high-level LangGraph, Deep Agents, and agenthub—not single-agent loops (agentic-ai-developer), ML training (ai-engineer), strategy-only whiteboard (enterprise-strategist), or PM planning (technical-program-manager). Use for multi-agent system, multi-agent engineer, agent orchestration, supervisor agent, agent topology, fan-out fan-in, agent handoff protocol, multi-agent workflow, agent coordination, blackboard pattern, hierarchical agents, A2A, agent DAG, multi-agent architecture.
Guides architectural decisions for LangGraph applications. Use when deciding between LangGraph vs alternatives, choosing state management strategies, designing multi-agent systems, or selecting persistence and streaming approaches.
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration.