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Found 156 Skills
Audit a skill repository or installed skill collection for global consistency, lifecycle coverage, routing quality, documentation drift, memory writeback coverage, stale future-skill references, broken helper paths, and validation readiness. Use this skill whenever the user asks for a global consistency audit, skill taxonomy review, lifecycle audit, cross-skill routing audit, README or AGENTS inventory consistency check, or maintenance pass over a collection of agent skills.
Track, optimize, and control token consumption across multi-agent systems. Covers budget allocation, real-time monitoring, cost attribution, per-agent limits, and proactive cost optimization for production LLM deployments.
Control Room template for managing Hermes agents from one VPS agent to specialist teams and orchestrated workflows
Step-by-step guide to building AI agents from simple chat loops to autonomous multi-agent systems with tools, memory, and event-driven architecture
Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent context.
Create the brain from scratch — the gateway skill. If AI First OS is not present it installs the complete system first, then interviews the operator with five questions and writes the minimum node structure — identity, voice, root resolver, the tree of paths a scan walks, and one folder per organization. Manually triggered, once per brain, before any other tool.
Multi-agent distributed context preservation protocol using cryptographic sharding, gossip propagation, and Byzantine fault tolerance to maintain coherent shared memory across dynamic agent networks.
Эксперт по оркестрации AI агентов. Используй для multi-agent systems, agent coordination, task delegation и agent workflows.
Self-evolving AI agent system with 26 tools, three-layer memory, MCP plugins, and 24/7 self-repair in pure Python.
Spawn specialized sub-agents with context handoff for complex multi-phase tasks. Enables expertise delegation within a session with automatic context merging and depth limiting to prevent infinite loops.
Build type-safe LLM applications with DSPy.rb — Ruby's programmatic prompt framework with signatures, modules, agents, and optimization. Use when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers, building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.
Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.