- 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
- Get an agent recommendation —
mcp__claude-flow__hooks_routewith the task description. Returns{ recommended, confidence, reasoning }. - Get a model tier recommendation —
mcp__claude-flow__hooks_model-routefor Haiku/Sonnet/Opus selection. - Search for similar past patterns —
mcp__claude-flow__hooks_intelligence_pattern-searchto find prior successes. - Predict outcome —
mcp__claude-flow__neural_predictwith the task description for a confidence-scored prediction. - Spawn the recommended agent at the recommended model tier.
- (If
--whywas passed) — callmcp__claude-flow__hooks_explainto surface the routing rationale to the user. - After task completes — call
mcp__claude-flow__hooks_model-outcomewithsuccess: true|falseto 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"