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Found 424 Skills
Design and engineer System Prompts, prompt templates, and multi-agent orchestration contracts for deterministic, leak-proof AI systems. Use when creating agents, writing skill definitions, designing prompt templates with safe variable injection, structuring I/O contracts, or building multi-agent pipelines.
Self-hosted web dashboard for managing Hermes AI agent stacks with terminals, file explorer, multi-agent gateway, and RBAC
Declarative workflow orchestration for multi-agent tasks. Activate when users need to coordinate multiple agent jobs, run parallel tasks, or create reusable automation pipelines.
Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).
Bounded, evidence-driven Loop Engineering for Codex and Claude Code. Use to design, run, resume, or audit a coding loop that requires repeated implementation and verification, multi-agent routing, checkpoint recovery, measurable improvement, deterministic budgets, or explicit stop conditions.
Use when user says "create workflow", "create a workflow", "design workflow", "orchestrate", "automate multiple steps", "coordinate agents", "multi-agent workflow". Creates orchestration workflows from natural language using Socratic questioning to plan multi-agent workflows with visualization.
Build production-ready AI workflows using Firebase Genkit. Use when creating flows, tool-calling agents, RAG pipelines, multi-agent systems, or deploying AI to Firebase/Cloud Run. Supports TypeScript, Go, and Python with Gemini, OpenAI, Anthropic, Ollama, and Vertex AI plugins.
Ultimate multi-agent framework for Google Antigravity. Orchestrates specialized domain agents (PM, Frontend, Backend, Mobile, QA, Debug) via Serena Memory.
oh-my-claudecode — Teams-first multi-agent orchestration layer for Claude Code. 32 specialized agents, smart model routing, persistent execution loops, and real-time HUD visibility. Zero learning curve.
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
Jeffrey Emanuel's multi-agent implementation workflow using NTM, Agent Mail, Beads, and BV. The execution phase that follows planning and bead creation. Includes exact prompts used.
Use this skill when you see `/omo`. Multi-agent orchestration for "code analysis / bug investigation / fix planning / implementation". Choose the minimal agent set and order based on task type + risk; recipes below show common patterns.