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__plugin_ruflo-core_ruflo__hooks_route with the task description. Returns { recommended, confidence, reasoning }.
2. Get a model tier recommendation — mcp__plugin_ruflo-core_ruflo__hooks_model-route for Haiku/Sonnet/Opus selection.
3. Search for similar past patterns — mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-search to find prior successes.
4. Predict outcome — mcp__plugin_ruflo-core_ruflo__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__plugin_ruflo-core_ruflo__hooks_explain to surface the routing rationale to the user.
7. After task completes — call mcp__plugin_ruflo-core_ruflo__hooks_model-outcome with success: true|false to train the router.

3-Tier Model Routing

Tier	Handler	Latency	Cost	When
1	Deterministic codemod (TS compiler)	~1ms	$0	Structural transforms with no LLM: var-to-const, remove-console, add-logging
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 [CODEMOD_AVAILABLE] for a deterministic intent (var-to-const, remove-console, add-logging), call mcp__plugin_ruflo-core_ruflo__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"