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name: Composition AI Research description: Research product compositions using AI and web sources. Use when needing to find what products, substances, or objects are made of - including ingredients, materials, chemicals, and elements. Essential for building composition data.

Composition AI Research Skill

Purpose

This skill enables thorough research into what things are made of. Use it to gather accurate composition data for any product, substance, organism, or object.

When to Use

• User asks to research a composition
• Building the composition database
• Verifying or updating existing composition data
• Finding sources for composition claims

Research Process

Step 1: Identify the Subject

Clarify exactly what we're researching:

• Product name, brand, variant (e.g., "Kellogg's Frosted Flakes 13.5oz box")
• Category (food, electronics, biological, chemical, etc.)
• Any specific version or configuration

Step 2: Source Priority

Research in this order:

1. Official Sources (highest confidence)

   • Manufacturer websites
   • FDA ingredient databases
   • Safety Data Sheets (SDS/MSDS)
   • Nutrition labels
   • Patent filings

2. Scientific Sources

   • PubChem for chemical data
   • Scientific papers
   • MaterialsProject for materials
   • Industry technical specs

3. Analysis Sources

   • iFixit teardowns (electronics)
   • Independent lab testing
   • Engineering analysis sites
   • Consumer reports

4. Secondary Sources (verify independently)

   • Wikipedia (check sources)
   • News articles
   • Industry reports

Step 3: Data Structure

Organize findings hierarchically:

CopyLevel 1: Product (iPhone 15 Pro)
  └── Level 2: Component (Battery)
        └── Level 3: Material (Lithium-ion cell)
              └── Level 4: Chemical (Lithium cobalt oxide)
                    └── Level 5: Element (Li, Co, O)

Step 4: Confidence Assessment

For each data point:

• Verified: Direct from official source with citation
• Estimated: Based on similar products or industry standards
• Speculative: Reasonable inference when data unavailable

Output Format

Return research as structured JSON:

Copy{
  "subject": {
    "name": "Product Name",
    "category": "Category",
    "variant": "Specific variant if applicable"
  },
  "composition": [
    {
      "name": "Component Name",
      "percentage": 45.2,
      "confidence": "verified",
      "source": "https://source-url.com",
      "type": "component",
      "children": []
    }
  ],
  "sources": [
    {
      "url": "https://...",
      "title": "Source Title",
      "type": "official|scientific|analysis|secondary",
      "accessed": "2024-01-15"
    }
  ],
  "notes": "Any caveats or limitations"
}

Common Research Patterns

Food Products

1. Start with nutrition facts label
2. FDA Food Composition Database
3. Research each ingredient's chemical makeup
4. Track to molecular/elemental level

Electronics

1. Search for teardown reports
2. Check manufacturer sustainability reports
3. Research battery chemistry specifically
4. Patents often reveal proprietary details

Chemicals

1. PubChem for molecular structure
2. SDS for composition percentages
3. ChemSpider for additional data
4. Scientific literature for variations

Biological

1. Scientific databases (UniProt, NCBI)
2. Peer-reviewed papers
3. Consider hydrated vs dry weight
4. Note species-specific variations

Quality Guidelines

1. Always cite sources - Every percentage needs a source
2. Use ranges when uncertain - "40-50%" better than guessing "45%"
3. Note proprietary limitations - Some data is trade secret
4. Cross-reference multiple sources - Don't rely on single source
5. Date your research - Compositions can change over time