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Found 26 Skills
VoltAgent architectural patterns and conventions. Covers agents vs workflows, project layout, memory, servers, and observability.
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature.
Comprehensive guide to understanding and implementing AI agent systems using Claude Code architecture patterns
Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).
Expert prompt engineering for LLM applications including prompt design, optimization, RAG systems, agent architectures, and AI product development.
Self-improving agent architecture using ChromaDB for continuous learning, self-evaluation, and improvement storage. Agents maintain separate memory collections for learned patterns, performance metrics, and self-assessments without modifying their static .md configuration.
Perform 12-Factor Agents compliance analysis on any codebase. Use when evaluating agent architecture, reviewing LLM-powered systems, or auditing agentic applications against the 12-Factor methodology.
Use when a single agent demonstrably cannot handle the task and multi-agent coordination is justified.
Deep architectural knowledge of AI Agent Harness design patterns, implementation strategies, and Claude Code internals for building production-grade AI agents
Deep expertise in Hermes Agent architecture, implementation patterns, and extension development
Deep Agents framework — architectural decisions (when to use Deep Agents vs alternatives, backend strategies, subagent design, middleware approaches) AND code review (bugs, anti-patterns, improvements when reviewing Deep Agents code). Use when working with Deep Agents — designing a new system or reviewing existing code.
Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.