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Intelligence Route

Agent Building
intelligence-route

Intelligence Route is an Agent Skill for agent building published in the ruvnet/ruflo repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Route tasks via the 3-tier model selector and learned patterns; emits a routing rationale via hooks_explain

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How to use this skill

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Prompt to paste
Inspect the Agent Skill at https://github.com/ruvnet/ruflo/blob/HEAD/plugins/ruflo-intelligence/skills/intelligence-route/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/intelligence-route, but wait for my approval before writing files or running scripts.

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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

TierHandlerLatencyCostWhen
1Deterministic codemod (TS compiler)~1ms$0Structural transforms with no LLM: var-to-const, remove-console, add-logging
2Haiku~500ms~$0.0002Low complexity (<30%), bug fixes, quick patches
3Sonnet/Opus2–5s$0.003–$0.015Complex reasoning, architecture, security, multi-file refactors

When hooks_route returns [CODEMOD_AVAILABLE] for a deterministic intent (var-to-const, remove-console, add-logging), call mcp__claude-flow__hooks_codemod with the intent + file — it applies the transform via the TypeScript compiler at $0, no LLM. Note: add-types, add-error-handling, async-await require judgement and route to a model (Tier 2/3) per ADR-143; they are NOT $0 codemods. Agent Booster is a fast-apply merge engine for LLM-produced edits, not the Tier-1 path.

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"