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Composition AI Research

Identify product, substance, or object compositions with hierarchical JSON output and source citations.

Updated Aug 27, 2026
Search Tags:#verification#composition#citation#sources#ai-research#data-sourcing
One-click install
npx skills add https://github.com/nategarelik/composition --skill composition-ai-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Composition AI Research
Source: https://github.com/nategarelik/composition/tree/main/.claude/skills/ai-research
Command: npx skills add https://github.com/nategarelik/composition --skill composition-ai-research

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps researchers gather accurate composition data from reliable sources using AI, reducing manual search time and ensuring structured results.

Core Features & Use Cases

  • Source-first research: Prioritize official and scientific sources to build a credible composition record.
  • Structured data output: Produce hierarchical composition data with confidence levels and sources.
  • Use Case: Build a composition database for product catalogs by researching the subject’s name, category, and materials, then storing results with sources.
  • Verification: Annotate each data point with confidence levels (verified, estimated, speculative) and source citations.

Quick Start

Provide the subject to research, for example "Kellogg's Frosted Flakes", and instruct the AI to gather ingredients, materials, and elemental data from primary sources.

Frequently Asked Questions about Composition AI Research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I gather product composition data from reliable sources?▼

Composition data gathering identifies ingredients, materials, and elements using AI-validated research from official and scientific sources. This Skill structures results hierarchically with confidence levels (verified, estimated, speculative) and explicit source citations, reducing manual search time while ensuring credibility.

What's the best way to verify and cite sources when researching product ingredients?▼

Source verification annotates each data point with confidence tags and primary source citations. This Skill enforces hierarchical data modeling with dated entries, ensuring researchers can trace ingredients back to authoritative sources and distinguish verified facts from estimates.

Can I build a composition database with structured output and confidence levels?▼

Yes. This Skill produces hierarchical JSON-like structures containing composition data, confidence annotations, and source provenance. It's designed for product catalogs, materials research, and elemental analysis where structured, traceable data is required.

How do I compile materials and chemicals data with validation from primary sources?▼

Composition research collects materials, chemicals, or elements through stepwise AI-guided gathering that prioritizes official documentation. Output includes hierarchical organization, confidence tagging, and explicit source links, validating each component against reliable authorities.

What's included in a structured composition output?▼

Structured composition output contains hierarchical data elements with confidence levels, source citations, collection dates, and categorical organization. This format supports traceability, reproducibility, and cross-referencing for foods, electronics, chemicals, and biological items.

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