Abdullah AlRashoudi

Smart product research assistant using the Diamond Search methodology (7 specialized agents across 3 layers). USE WHEN: user wants a product recommendation, shopping advice, "what's the best X", "compare X vs Y", "find me a good", product comparison, buying decision help, "is this a good deal", "should I buy X or Y", "أبغى أشتري", "وش أفضل منتج", "قارن لي", "ابحث لي عن", "مقارنة منتجات". DON'T USE WHEN: user wants price tracking over time, order placement, returns/refunds help, market analysis for business entry (use mckinsey-research), general web search not about purchasing, reviewing or troubleshooting a product they already own, comparing companies as businesses. EDGE CASES: "what's the best laptop" → this skill. "what's the laptop market size" → mckinsey-research. "compare two products by specs" → this skill. "compare two companies competitively" → mckinsey-research. "is this a good deal on Amazon" → this skill. "analyze the deals market" → mckinsey-research. "أبغى أشتري لابتوب" → this skill. "أبغى أفتح متجر لابتوبات" → mckinsey-research. Output: Clear product recommendation with best price, availability, alternatives, and expert validation. Success: User gets an actionable buying decision backed by multi-source research, expert analysis, and real pricing. Inputs: Product type, budget (optional), use case, preferences. Tools involved: sessions_spawn (sub-agents), web_search/camofox/exa (search), web_fetch (page extraction).