# smart-routing

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

- Skill: `a5c-ai/smart-routing` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add a5c-ai/smart-routing`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/smart-routing/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-08-19
- Page: https://skillmd.com/skills/a5c-ai/smart-routing

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- 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`