Complexity-based task routing with Q-Learning optimization, Agent Booster WASM fast-path, and Mixture-of-Experts model selection.
npx claudepluginhub a5c-ai/babysitterThis skill is limited to using the following tools:
README.mdIntelligent task routing using Q-Learning to select optimal execution paths. Simple tasks route to Agent Booster (WASM, <1ms, $0), medium tasks to efficient models, and complex tasks to Opus + multi-agent swarms.
| Tier | Target | Latency | Cost |
|---|---|---|---|
| Agent Booster | Simple transforms (var-to-const, add-types) | <1ms | $0 |
| Medium | Standard coding tasks | ~500ms | Low |
| Complex | Multi-agent swarm coordination | 2-5s | Higher |
var-to-const - Variable declaration modernizationadd-types - TypeScript type annotation insertionadd-error-handling - Try/catch wrapper insertionasync-await - Promise chain to async/await conversionextract-function - Code block extraction to named functionsadd-jsdoc - Documentation generationagents/optimizer/ - Performance and cost optimizationagents/architect/ - Complex task decompositionInvoke via babysitter process: methodologies/ruflo/ruflo-task-routing
Activates when the user asks about AI prompts, needs prompt templates, wants to search for prompts, or mentions prompts.chat. Use for discovering, retrieving, and improving prompts.
Search, retrieve, and install Agent Skills from the prompts.chat registry using MCP tools. Use when the user asks to find skills, browse skill catalogs, install a skill for Claude, or extend Claude's capabilities with reusable AI agent components.
This skill should be used when the user wants to "create a skill", "add a skill to plugin", "write a new skill", "improve skill description", "organize skill content", or needs guidance on skill structure, progressive disclosure, or skill development best practices for Claude Code plugins.