agent-management
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents",…
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
Claude Code
$ npx -y skills add davila7/claude-code-templates --skill autonomous-agents --agent claude-code
Codex
$ npx skills add davila7/claude-code-templates --skill autonomous-agents -a codex -y
Cursor
$ npx skills add davila7/claude-code-templates --skill autonomous-agents -a cursor -y
Gemini CLI
$ npx skills add davila7/claude-code-templates --skill autonomous-agents -a gemini-cli -y
GitHub Copilot
$ npx skills add davila7/claude-code-templates --skill autonomous-agents -a github-copilot -y
OpenCode
$ npx skills add davila7/claude-code-templates --skill autonomous-agents -a opencode -y
Windsurf
$ npx skills add davila7/claude-code-templates --skill autonomous-agents -a windsurf -y
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" metadata: 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`
Open-source registry and CLI for Claude Code components: agents, commands, skills, hooks, MCPs, settings and mods
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents",…
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user…
Search auto-generated codebase documentation for function signatures, API docs, class definitions, and code comments. Use when…
Query the code graph database to understand component relationships, dependencies, and change impact. Use when the user asks to…
Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to…
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
Ask which skill or flow fits your situation. A router over the skills in this repo.