Agent-Skills.md

Agent Skills: smart-routing

Complexity-based task routing with Q-Learning optimization, Agent Booster WASM fast-path, and Mixture-of-Experts model selection.

UncategorizedID: a5c-ai/babysitter/smart-routing

Repository

a5c-ai/babysitter
a5c-aiLicense: MIT
1,52087

Install this agent skill to your local

pnpm dlx add-skill https://github.com/a5c-ai/babysitter/tree/HEAD/library/methodologies/ruflo/skills/smart-routing

Skill Files

Browse the full folder contents for smart-routing.

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library/methodologies/ruflo/skills/smart-routing/SKILL.md

Skill Metadata

Name
smart-routing
Description
Complexity-based task routing with Q-Learning optimization, Agent Booster WASM fast-path, and Mixture-of-Experts model selection.
  • When tasks range from simple transforms to complex multi-file changes
  • Reducing latency for common code transformations
  • Learning from routing history to improve future decisions

Routing Tiers

| 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 |

Agent Booster Transforms

  • var-to-const - Variable declaration modernization
  • add-types - TypeScript type annotation insertion
  • add-error-handling - Try/catch wrapper insertion
  • async-await - Promise chain to async/await conversion
  • extract-function - Code block extraction to named functions
  • add-jsdoc - Documentation generation

Agents Used

  • agents/optimizer/ - Performance and cost optimization
  • agents/architect/ - Complex task decomposition

Tool Use

Invoke via babysitter process: methodologies/ruflo/ruflo-task-routing