intelligence-route

name	intelligence-route
description	Route tasks via the 3-tier model selector and learned patterns; emits a routing rationale via hooks_explain
argument-hint	<task-description> [--why]
allowed-tools	mcp__claude-flow__hooks_route mcp__claude-flow__hooks_explain mcp__claude-flow__hooks_model-route mcp__claude-flow__hooks_model-stats mcp__claude-flow__hooks_model-outcome mcp__claude-flow__hooks_intelligence_pattern-search mcp__claude-flow__hooks_intelligence_attention mcp__claude-flow__hooks_intelligence_stats mcp__claude-flow__neural_predict mcp__claude-flow__hooks_pre-task Bash

Intelligence Routing

Pick the optimal agent + model tier for a task using learned patterns + the 3-tier router. Emits a hooks_explain rationale so the choice is auditable.

When to use

Before starting any non-trivial task. Replaces manual agent selection with data-driven decisions.

Steps

1. Get an agent recommendation — mcp__claude-flow__hooks_route with the task description. Returns { recommended, confidence, reasoning }.
2. Get a model tier recommendation — mcp__claude-flow__hooks_model-route for Haiku/Sonnet/Opus selection.
3. Search for similar past patterns — mcp__claude-flow__hooks_intelligence_pattern-search to find prior successes.
4. Predict outcome — mcp__claude-flow__neural_predict with the task description for a confidence-scored prediction.
5. Spawn the recommended agent at the recommended model tier.
6. (If --why was passed) — call mcp__claude-flow__hooks_explain to surface the routing rationale to the user.
7. After task completes — call mcp__claude-flow__hooks_model-outcome with success: true|false to train the router.

3-Tier Model Routing

Tier	Handler	Latency	Cost	When
1	Agent Booster (WASM)	<1ms	$0	Simple transforms (var→const, add types, remove console) — skip LLM entirely
2	Haiku	~500ms	~$0.0002	Low complexity (<30%), bug fixes, quick patches
3	Sonnet/Opus	2–5s	$0.003–$0.015	Complex reasoning, architecture, security, multi-file refactors

When hooks_route returns [AGENT_BOOSTER_AVAILABLE] for an intent type (var-to-const, add-types, add-error-handling, async-await, add-logging, remove-console), skip the LLM and use the Edit tool directly.

Recording outcomes

Closing the routing loop is mandatory:

# Success
mcp tool call hooks_model-outcome --json -- '{"taskId": "T123", "success": true, "model": "haiku"}'

# Failure with reason
mcp tool call hooks_model-outcome --json -- '{"taskId": "T123", "success": false, "model": "haiku", "reason": "complexity-misjudged"}'

The router learns from these calls. Skipping them = no learning.

CLI alternative

npx @claude-flow/cli@latest hooks route --task "description"
npx @claude-flow/cli@latest hooks pre-task --description "description"
npx @claude-flow/cli@latest hooks explain --topic "routing decision"