Multi Model Discovery

Multi-Model Discovery Skill

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

Purpose

Use Gemini CLI's Google Search grounding capability to discover existing solutions before implementing from scratch. This skill embodies the principle: "Don't reinvent the wheel."

When to Use This Skill

• Before implementing a new feature (find existing solutions first)
• When researching best practices for a technology
• When looking for code examples or patterns
• When evaluating libraries or frameworks
• When unsure if a problem has already been solved
• Before writing boilerplate code that might exist

When NOT to Use This Skill

• For implementation tasks (use codex-iterative-fix instead)
• When you already know the solution exists in the codebase
• For debugging existing code (use smart-bug-fix)
• For codebase analysis (use gemini-codebase-onboard)

Workflow

Phase 1: Research Query Formulation

1. Analyze the implementation goal
2. Formulate search queries for:
   • Existing libraries/packages
   • Code examples on GitHub
   • Best practice guides
   • Common patterns

Phase 2: Gemini Discovery Execution

# Execute via delegate.sh wrapper
./scripts/multi-model/delegate.sh gemini "Find existing solutions for: {goal}"

# Or via gemini-yolo.sh
./scripts/multi-model/gemini-yolo.sh "How do others implement {feature}? Find code examples and libraries." task-id research

Phase 3: Results Synthesis

1. Claude synthesizes Gemini's findings
2. Evaluate options:
   • Use existing library
   • Adapt existing pattern
   • Build from scratch (last resort)
3. Document decision rationale

Success Criteria

• Existing solution found and evaluated
• Build vs buy decision made with evidence
• Time saved by avoiding reinvention
• Quality improved by using proven patterns

Example Usage

Example 1: Auth Implementation

User: "Implement user authentication"

Discovery Process:
1. Gemini search: "What are best practices for auth in Node.js?"
2. Gemini search: "Find existing auth libraries: passport, next-auth, lucia"
3. Gemini search: "Code examples for JWT authentication Node.js"

Output:
- Recommended: next-auth (well-maintained, 40k+ stars)
- Alternative: lucia-auth (newer, type-safe)
- Pattern found: middleware-based validation

Example 2: PDF Generation

User: "Generate PDF reports from data"

Discovery Process:
1. Gemini search: "PDF generation libraries JavaScript 2024"
2. Gemini search: "Compare pdfkit vs puppeteer vs react-pdf"
3. Gemini search: "Production PDF generation best practices"

Output:
- Simple PDFs: pdfkit (lightweight)
- Complex layouts: puppeteer (HTML to PDF)
- React apps: react-pdf

Integration with Meta-Loop

META-LOOP PROPOSE PHASE:
    |
    +---> multi-model-discovery
    |         |
    |         +---> Gemini: Find existing solutions
    |         +---> Claude: Evaluate options
    |         +---> Decision: Build vs Adapt vs Use
    |
    +---> Continue to IMPLEMENT phase

Memory Integration

Results stored at:

• Key: multi-model/discovery/{project}/{task_id}
• Tags: WHO=multi-model-discovery, WHY=avoid-reinvention

Invocation Pattern

# Via router (automatic detection)
./scripts/multi-model/multi-model-router.sh "Find existing solutions for X"

# Direct Gemini call
bash -lc "gemini 'How do others implement X? Find code examples and libraries.'"

Related Skills

• gemini-research: General research with search grounding
• gemini-megacontext: Full codebase analysis
• codex-iterative-fix: After discovery, for implementation
• literature-synthesis: Academic research synthesis

Verification Checklist

• Gemini search executed with clear queries
• Multiple solutions discovered and compared
• Build vs buy decision documented
• Memory-MCP updated with findings
• Decision rationale captured