Multi Model

/============================================================================/ /* 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

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:

# 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]