agent-management
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",…
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This
$ npx -y skills add davila7/claude-code-templates --skill autonomous-agents --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/autonomous-agentsContext preview
The summary Claude sees to decide when to auto-load this skill.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This
name: autonomous-agents description: "Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b" source: vibeship-spawner-skills (Apache 2.0)
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
Alternating reasoning and action steps
Separate planning phase from execution
Self-evaluation and iterative improvement
| 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 |
Works well with: `agent-tool-builder`, `agent-memory-systems`, `multi-agent-orchestration`, `agent-evaluation`
Ready-to-use configurations for Anthropic's Claude Code. A comprehensive collection of AI agents, custom commands, settings, hooks, external integrations (MCPs), and project templates to enhance your development workflow.
Repo: davila7/claude-code-templates
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",…
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…
Search auto-generated codebase documentation for function signatures, API docs, class definitions, and code comments. Use when the user asks to "search docs",…
Query the code graph database to understand component relationships, dependencies, and change impact. Use when the user asks to "find callers", "check…
Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to "search memory", "what did we…