How do I install the Intelligence Route skill?

Run npx skillmds@latest add gabrielmoreira/intelligence-route in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Intelligence Route safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Intelligence Route?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Intelligence Route free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Intelligence Route?

gabrielmoreira (@gabrielmoreira) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Intelligence Route by gabrielmoreira

Name: Intelligence Route
Rating: 4.2 (5 reviews)
Author: gabrielmoreira

File contents

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__plugin_ruflo-core_ruflo__hooks_route with the task description. Returns { recommended, confidence, reasoning }.
Get a model tier recommendation — mcp__plugin_ruflo-core_ruflo__hooks_model-route for Haiku/Sonnet/Opus selection.
Search for similar past patterns — mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-search to find prior successes.
Predict outcome — mcp__plugin_ruflo-core_ruflo__neural_predict with the task description for a confidence-scored prediction.
Spawn the recommended agent at the recommended model tier.
(If --why was passed) — call mcp__plugin_ruflo-core_ruflo__hooks_explain to surface the routing rationale to the user.
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"

---name: intelligence-routedescription: Route tasks via the 3-tier model selector and learned patterns; emits a routing rationale via hooks_explain--- # 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: ```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" ```

Frequently asked questions

Intelligence Route