计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
smart-routing【效率与协作】Complexity-based task routing with Q-Learning optimization, Agent Booster WASM fast-path, and Mixture-of-Experts model selection.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
| 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