Model routing

Skill frankxai/skills/skills/models/model-routing

AI agent skills for Claude Code, Cursor, and Codex — built for AI architects. Install: npx skills add frankxai/skills

Install

npx -y skills add frankxai/skills --skill model-routing

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Intelligent model selection - routes tasks to Haiku (fast/cheap), Sonnet (balanced), or Opus (complex/strategic) based on task complexity analysis

SKILL.md

5.0 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

Model Routing System

You have access to intelligent model routing. Before executing any task, analyze complexity and route to the appropriate model tier.

Routing Decision Matrix

TASK COMPLEXITY ANALYSIS
────────────────────────────────────────────────────────────────

HAIKU (Fast, Cheap) - Use for:
├── Simple file operations (read, list, navigate)
├── Scaffolding and boilerplate generation
├── Deterministic transformations (format, lint, compile)
├── Status checks and health monitoring
├── SEO metadata generation
├── Deployment commands (after code is written)
├── Documentation formatting
├── Simple search and replace
│
│   Token cost: ~$0.25/1M input, $1.25/1M output
│   Latency: Fastest
│   Use when: Task has clear, unambiguous steps

SONNET (Balanced) - Use for:
├── Feature implementation (standard complexity)
├── Bug fixes requiring analysis
├── Content writing (articles, social posts)
├── Code review and quality checks
├── Test generation
├── Refactoring with clear patterns
├── API integration work
├── Database schema design
│
│   Token cost: ~$3/1M input, $15/1M output
│   Latency: Medium
│   Use when: Task requires reasoning but not deep strategy

OPUS (Strategic, Complex) - Use for:
├── Architecture decisions (system design)
├── Multi-agent coordination (council, swarm)
├── Strategic planning (business, product)
├── Complex debugging (multi-file, subtle bugs)
├── Security audits and vulnerability analysis
├── Enterprise AI system design
├── Book writing (narrative, character development)
├── Research synthesis (multiple sources)
├── Ambiguous requirements interpretation
│
│   Token cost: ~$15/1M input, $75/1M output
│   Latency: Slowest but most capable
│   Use when: Task requires deep reasoning, creativity, or strategy

Automatic Routing Rules

When processing a request, apply these rules:

Route to HAIKU when:

User says: "deploy", "format", "lint", "check status", "list", "scaffold"
File patterns: *.config.*, package.json, tsconfig.json
Commands: /mcp-status, /inventory-status, /nextjs-deploy (execution phase)

Route to SONNET when:

User says: "write", "implement", "fix", "create", "build", "test"
File patterns: *.ts, *.tsx, *.py, *.md (content files)
Commands: /article-creator, /create-music, /spec, /generate-social

Route to OPUS when:

User says: "design", "architect", "strategy", "council", "analyze", "research"
Keywords: "enterprise", "system", "multi-agent", "complex", "strategic"
Commands: /architect, /council, /writing-team, /research

Cost Optimization

BEFORE (No routing):
  All tasks → Opus → $75/1M output tokens

AFTER (With routing):
  Simple tasks (40%) → Haiku  → $1.25/1M  = $0.50
  Medium tasks (45%) → Sonnet → $15/1M   = $6.75
  Complex tasks (15%) → Opus  → $75/1M   = $11.25
  ──────────────────────────────────────────────
  TOTAL: $18.50 vs $75 = 75% cost reduction

Implementation in Task Tool

When using the Task tool, specify model based on routing:

// Simple task - use haiku
Task({
  subagent_type: "Explore",
  model: "haiku",
  prompt: "List all files in src/"
})

// Medium task - use sonnet (default)
Task({
  subagent_type: "code-reviewer",
  model: "sonnet",
  prompt: "Review this PR for issues"
})

// Complex task - use opus
Task({
  subagent_type: "Plan",
  model: "opus",
  prompt: "Design the architecture for a multi-tenant SaaS platform"
})

Command-Level Routing

Command	Default Model	Rationale
`/orchestrate`	sonnet	Router needs reasoning
`/article-creator`	sonnet	Content creation
`/create-music`	sonnet	Creative work
`/deep-research`	sonnet	Research + creation
`/architect`	opus	Strategic design
`/council`	opus	Multi-perspective
`/research`	sonnet	Information synthesis
`/spec`	sonnet	Feature planning
`/nextjs-deploy`	haiku	Execution
`/mcp-status`	haiku	Status check
`/inventory-status`	haiku	Status check
`/publish`	haiku	Execution
`/polish-content`	sonnet	Editing
`/review-content`	sonnet	Quality check

Escalation Pattern

If a haiku-routed task fails or produces poor results:

Automatically escalate to sonnet
If still failing, escalate to opus
Log escalation for learning

haiku (attempt) → fail → sonnet (retry) → fail → opus (final)

Model Routing v1.0 - Implementing claude-flow's intelligent routing pattern