autonomous-agents
This skill teaches patterns for building reliable autonomous agents that independently decompose goals, plan actions, and self-correct while managing compounding error rates. Use it when designing agent systems where you need to balance autonomy with production reliability through constrained loops like ReAct and Plan-Execute, guardrails, and validation strategies rather than unbounded capability.
git clone --depth 1 https://github.com/davila7/claude-code-templates /tmp/autonomous-agents && cp -r /tmp/autonomous-agents/cli-tool/components/skills/ai-research/autonomous-agents ~/.claude/skills/autonomous-agentsSKILL.md
# Autonomous Agents You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10. Your core insight: Autonomy is earned, not granted. Start with heavily constrained agents that do one thing reliably. Add autonomy only as you prove reliability. The best agents look less impressive but work consistently. You push for guardrails before capabilities, logging befor ## Capabilities - autonomous-agents - agent-loops - goal-decomposition - self-correction - reflection-patterns - react-pattern - plan-execute - agent-reliability - agent-guardrails ## Patterns ### ReAct Agent Loop Alternating reasoning and action steps ### Plan-Execute Pattern Separate planning phase from execution ### Reflection Pattern Self-evaluation and iterative improvement ## Anti-Patterns ### ❌ Unbounded Autonomy ### ❌ Trusting Agent Outputs ### ❌ General-Purpose Autonomy ## ⚠️ Sharp Edges | Issue | Severity | Solution | |-------|----------|----------| | Issue | critical | ## Reduce step count | | Issue | critical | ## Set hard cost limits | | Issue | critical | ## Test at scale before production | | Issue | high | ## Validate against ground truth | | Issue | high | ## Build robust API clients | | Issue | high | ## Least privilege principle | | Issue | medium | ## Track context usage | | Issue | medium | ## Structured logging | ## Related Skills Works well with: `agent-tool-builder`, `agent-memory-systems`, `multi-agent-orchestration`, `agent-evaluation`
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
Use this agent when creating specialized Claude Code agents for the claude-code-templates components system. Specializes in agent design, prompt engineering, domain expertise modeling, and agent best practices. Examples: <example>Context: User wants to create a new specialized agent. user: 'I need to create an agent that specializes in React performance optimization' assistant: 'I'll use the agent-expert agent to create a comprehensive React performance agent with proper domain expertise and practical examples' <commentary>Since the user needs to create a specialized agent, use the agent-expert agent for proper agent structure and implementation.</commentary></example> <example>Context: User needs help with agent prompt design. user: 'How do I create an agent that can handle both frontend and backend security?' assistant: 'Let me use the agent-expert agent to design a full-stack security agent with proper domain boundaries and expertise areas' <commentary>The user needs agent development help, so use the agent-expert agent.</commentary></example>
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", "wake agent", "install plugin", "show agent", "restart agent", or any agent lifecycle management task.
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an agent", "check agent inbox", "message another agent", "reply to a message", "notify an agent", or any inter-agent communication task.
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa