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Intelligence Routing

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

Installable skillMITBy digitalcrest01Updated Oct 30, 2026
INTENDED FOROpenClawInstall in a listed agent.

instructions.md

---
name: intelligence-route-74059194a0de
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:

```bash
# 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

```bash
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"
```

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Platform guidance

Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.