# multi-model > /*============================================================================*/ /* SKILL SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ - Author: DNYoussef - Repository: DNYoussef/ruv-sparc-three-loop-system - Version: 20260113122214 - Stars: 0 - Forks: 0 - Last Updated: 2026-02-08 - Source: https://github.com/DNYoussef/ruv-sparc-three-loop-system - Web: https://mule.run/skillshub/@@DNYoussef/ruv-sparc-three-loop-system~multi-model:20260113122214 --- /*============================================================================*/ /* SKILL SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: SKILL version: 1.0.0 description: | [assert|neutral] Intelligent multi-model orchestrator that routes tasks to Gemini or Codex based on their strengths [ground:given] [conf:0.95] [state:confirmed] category: platforms tags: - orchestration - multi-model - routing - automation - gemini author: system cognitive_frame: primary: compositional goal_analysis: first_order: "Execute SKILL workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic platforms processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "SKILL", category: "platforms", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Compositional", source: "German", force: "Build from primitives?" } [ground:cognitive-science] [conf:0.92] [state:confirmed] ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. /*----------------------------------------------------------------------------*/ /* S2 TRIGGER CONDITIONS */ /*----------------------------------------------------------------------------*/ [define|neutral] TRIGGER_POSITIVE := { keywords: ["SKILL", "platforms", "workflow"], context: "user needs SKILL capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ # Multi-Model Orchestrator Skill ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. ## Purpose Automatically route tasks to the optimal AI model (Gemini or Codex) based on task requirements and each model's unique strengths. You don't need to decide - the orchestrator does it for you. ## How It Works The orchestrator analyzes your request and routes to: ### Gemini CLI → For: - **Mega-Context**: Large codebase analysis (30K+ lines) - **Web Search**: Real-time information needs - **Media Gen**: Image/video creation - **Extensions**: Figma, Stripe, Postman integrations ### Codex CLI → For: - **Full Auto**: Unattended prototyping/scaffolding - **Alternative Reasoning**: Second opinions, different approaches ### Claude Code → For: - **Everything Else**: Implementation, refinement, complex reasoning ## Usage ### Let Orchestrator Decide ``` /multi-model "I need to understand this 50K line codebase and create architecture diagrams" Orchestrator routes to: 1. gemini-megacontext (analyze codebase) 2. gemini-media (create diagrams) ``` ### Complex Multi-Step Tasks ``` /multi-model "Research React 19 best practices, prototype a dashboard, and generate UI mockups" Orchestrator routes to: 1. gemini-search (React 19 info) 2. codex-auto (prototype dashboard) 3. gemini-media (UI mockups) 4. Claude Code (integrate and refine) ``` ## Decision Matrix | Task Type | Routed To | Why | |-----------|-----------|-----| | Analyze entire codebase | gemini-megacontext | 1M token context | | Need current API docs | gemini-search | Web search grounding | | Create diagrams/videos | gemini-media | Imagen/Veo | | Figma/Stripe integration | gemini-extensions | Extension ecosystem | | Rapid prototyping | codex-auto | Full Auto mode | | Alternative solution | codex-reasoning | Different AI perspective | | Implementation/refinement | Claude Code | Best overall reasoning | ## Real Examples ### Example 1: New Project Setup ``` User: "Set up a new Next.js 15 project with best practices" Orchestrator: 1. gemini-search → Get Next.js 15 current best practices 2. codex-auto → Scaffold project structure 3. Claude Code → Customize and refine ``` ### Example 2: Codebase Migration ``` User: "Migrate this legacy codebase to TypeScript" Orchestrator: 1. gemini-megacontext → Analyze entire codebase structure 2. codex-auto → Auto-convert files to TypeScript 3. Claude Code → Fix type errors and refine ``` ### Example 3: Documentation Creation ``` User: "Create comprehensive documentation with visuals" Orchestrator: 1. gemini-megacontext → Understand architecture 2. gemini-media → Generate architecture diagrams 3. Claude Code → Write documentation content ``` ## Benefits ### Automatic Optimization - ✅ Uses each model's unique strengths - ✅ No need to remember which CLI does what - ✅ Optimal tool selection for each subtask - ✅ Coordinates multiple models seamlessly ### Cost Efficiency - ✅ Uses Gemini's free tier when appropriate (60/min, 1000/day) - ✅ Leverages your ChatGPT Plus subscription optimally - ✅ Uses Claude Code for what it does best ### Time Savings - ✅ Parallel execution when possible - ✅ No manual routing decisions - ✅ Automatic task decomposition ## Response Format The orchestrator provides: ```markdown # Multi-Model Task Orchestration ## Task Analysis [How the task was broken down] ## Routing Decisions 1. **gemini-megacontext**: [Why and what] 2. **codex-auto**: [Why and what] 3. **Claude Code**: [Why and what] ## Execution Plan [Step-by-step execution order] ## Results from Each Model ### Gemini Results [Output summary] ### Codex Results [Output summary] ### Claude Integration [How Claude combined/refined results] ## Final Deliverable [Combined, polished output] ``` ## When to Use ### Perfect For: ✅ Don't know which model to use ✅ Task spans multiple capabilities ✅ Want automatic optimization ✅ Complex multi-step workflows ✅ Learning which model does what ### Direct Skill Use Instead: Use specific sk /*----------------------------------------------------------------------------*/ /* S4 SUCCESS CRITERIA */ /*----------------------------------------------------------------------------*/ [define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S5 MCP INTEGRATION */ /*----------------------------------------------------------------------------*/ [define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S6 MEMORY NAMESPACE */ /*----------------------------------------------------------------------------*/ [define|neutral] MEMORY_NAMESPACE := { pattern: "skills/platforms/SKILL/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "SKILL-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S7 SKILL COMPLETION VERIFICATION */ /*----------------------------------------------------------------------------*/ [direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S8 ABSOLUTE RULES */ /*----------------------------------------------------------------------------*/ [direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* PROMISE */ /*----------------------------------------------------------------------------*/ [commit|confident] SKILL_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]