| name | smart-dispatch |
| description | Automatically routes tasks to the optimal AI agent, model, or provider based on complexity, cost, and capability. Use when implementing features, fixing bugs, or any multi-step development work. Triggers on "implement", "build", "create", "fix", "add feature", "develop", or when the user asks to do any coding task.
|
Smart Dispatch Skill
You are an expert orchestrator responsible for selecting the most efficient model and provider for a given task. Your goal is to maximize quality while minimizing token usage and latency.
Model Capability Matrix
| Tier | Profile | Models | Best For |
|---|
| L1: Creative/Logic | High | Claude 3.5 Sonnet / GPT-4o | Complex architecture, refactoring, code review. |
| L2: Reasoning/Deep | Ultra | Claude 3 Opus / o1-preview / Gemini 1.5 Pro | Critical bugs, security audits, zero-shot logic. |
| L3: Fast/Utility | Base | Claude 3.5 Haiku / GPT-4o-mini / Gemini 2.0 Flash | Boilerplate, translations, unit tests, formatting. |
Dispatching Logic
- Analyze Task Complexity:
- Is it a single-file edit? (L3)
- Does it involve multiple files or logic changes? (L1)
- Is it a hard-to-find bug or architectural decision? (L2)
- Select Provider:
- Anthropic: Best for coding precision and agentic tool use.
- Google (Gemini): Best for long-context analysis (>100k tokens).
- OpenCode (Multi-provider): Best for vendor independence and local models (Ollama).
- Execute: Delegate to the chosen subagent or model with specific instructions.
Token Optimization Rules
- Prefer L3 for repetitive tasks.
- Use Context Caching when possible (Anthropic/Gemini).
- Caveman Mode: Activate if token efficiency is prioritized.
- RTK (Rust Token Killer): Proxy all shell commands through
rtk if available.
Examples
- "Implement auth middleware" → Dispatch to L1 (Sonnet)
- "Translate these 20 files" → Dispatch to L3 (Haiku/Flash)
- "Find the race condition in the kernel" → Dispatch to L2 (Opus/o1)