intelligence-route
Route tasks via the 3-tier model selector and learned patterns; emits a routing rationale via hooks_explain
Best for Complex multi-step tasksWorks with GitHubLow risk
⌘source
- author
@ruvnet- repo
- ruvnet/ruflo
- language
- TypeScript
★46kstars
5.1kforks
May 5, 2026
✦overview.md
Key Features
- ·3-tier model selector
- ·Learned pattern search
- ·Auditable routing rationale
- ·Confidence-scored predictions
Use Cases
- →Starting a non-trivial task without knowing which agent or model to use
- →Deciding between Haiku/Sonnet/Opus for a given task
- →Auditing past routing decisions for team review
Best for
- ✓Complex multi-step tasks
- ✓Teams wanting auditable AI decisions
Not ideal for
- !Trivial or single-step tasks
- !When speed is critical (overhead of analysis)
FAQs
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 |
$install
1-click copynpx skills add ruvnet/ruflo --skill intelligence-routeSafety assessment
★
Clarity score
How clear and easy to understand the SKILL.md instructions are, rated from 1 to 5.
4/ 5
very goodClear and well structured, with only minor parts that might need a second read.
◎
Actionability score
How directly an agent can act on the SKILL.md instructions, rated from 1 to 5.
4/ 5
highMostly actionable with clear steps; only a few small gaps remain.