Loading...
Loading...
Found 13,244 Skills
Hermes-native AIOps agent for evidence-driven incident response, approval-gated remediation, and runbook learning
Local-first long-term memory for AI agents with symbolic short-term memory and layered long-term recall via a 4-tier progressive pipeline
Navigate the Hermes Agent ecosystem — skills, tools, integrations, deployment, and multi-agent orchestration resources
Local-first AI coding agent powered by llama.cpp with zero tokens costs, Docker sandboxing, 20 built-in tools, LSP/Roslyn intelligence, and MCP integration
Deep expertise in Hermes Agent architecture, implementation patterns, and extension development
Deploy Nemotron Voice Agent on Workstation (x86), Jetson Thor, or Cloud NIMs. Real-time speech-to-speech using NVIDIA ASR, TTS, LLM with WebRTC/WebSocket transport.
This skill should be used when the user asks to "create an agent", "make an agent", "write an agent", "build a subagent", "add an agent to a plugin", "design an autonomous agent", "generate an agent file", "write a system prompt for an agent", "what frontmatter does an agent need", "create a specialized agent". Not for skills or commands — use create-skill.
This skill should be used when the user asks to "repair an agent", "audit an agent", "fix my agent", "review agent quality", "check if my agent is well-written", "diagnose agent problems", "what's wrong with this agent", "improve this agent", or "what's wrong with this agent file". Not for skills — use repair-skill.
Run provider-agnostic live voice conversations with VAD, silence boundaries, wake-word gating, STT, and TTS through the AgentOS speech runtime.
Install and configure ktx, the self-improving context layer that teaches AI agents to query data warehouses accurately with approved metrics, semantic layer, and business knowledge.
Context layer for AI data agents - query warehouses accurately with semantic layers, metrics, and wiki knowledge through MCP
Invoke when the user asks to review, check, audit, or look over Qt6 C++ code — or suggest before committing. Runs deterministic linting (60+ rules) then six parallel deep- analysis agents covering model contracts, ownership, threading, API correctness, error handling, and performance. Reports only high-confidence issues (>80/100) with structured mitigations. Read-only — never modifies code.