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Schema Inference Skill

Infer schemas, data types, constraints, and relationships from datasets.

Updated Oct 29, 2025
Search Tags:#data analysis#type inference#data profiling#schema inference#constraint discovery#relationship detection
One-click install
npx skills add https://github.com/ksmuvva/Synthetic-data-generator --skill schema-inference-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Schema Inference Skill
Source: https://github.com/ksmuvva/Synthetic-data-generator/tree/main/.claude/skills/schema-inference
Command: npx skills add https://github.com/ksmuvva/Synthetic-data-generator --skill schema-inference-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex and time-consuming process of understanding data structures by automatically detecting schemas, inferring data types, identifying constraints, and uncovering relationships within datasets.

Core Features & Use Cases

  • Automatic Schema Detection: Infers schemas from raw data files without explicit definitions.
  • Intelligent Type Inference: Identifies primitive, complex, semantic, geographic, and business-specific data types.
  • Constraint Discovery: Detects nullability, uniqueness, ranges, lengths, patterns, and enumerations.
  • Relationship Inference: Uncovers foreign key, parent-child, and functional dependencies between fields.
  • Use Case: When provided with a CSV file containing customer transaction data, this Skill can automatically determine that 'email' is an email type, 'order_id' is a unique identifier, 'country' is a categorical field with specific values, and that 'postal_code' functionally determines 'city' and 'state'.

Quick Start

Use the schema inference skill to analyze the file 'customer_data.csv' and infer its schema.

Frequently Asked Questions about Schema Inference Skill

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

FAQPage Schema
How do I automatically detect schema and data types from a raw CSV file?▼

To automatically detect schema from a CSV file, you need schema inference to analyze raw data patterns without explicit definitions. This process identifies primitive, complex, semantic, geographic, and business-specific data types directly from the dataset contents.

What is data profiling and how does it identify constraints like nullability and uniqueness?▼

Data profiling through constraint discovery identifies nullability, uniqueness, value ranges, string lengths, patterns, and enumerations. This automated process scans your dataset to uncover structural rules and validate data integrity across all fields.

Can I automatically discover foreign key relationships and functional dependencies in my dataset?▼

Yes, relationship inference uncovers foreign key, parent-child, and functional dependencies between fields. For example, it can automatically determine that a postal_code field functionally determines both city and state values.

Does schema inference work with complex and semantic data types, or just primitive types?▼

Schema inference works with primitive, complex, semantic, geographic, and business-specific data types. It intelligently infers formats like email addresses and categorical country fields directly from raw data patterns.

What is the best way to infer business-specific data types from unstructured data patterns?▼

The best way to infer business-specific data types from unstructured patterns is using intelligent type inference. It automatically categorizes fields by analyzing data patterns, detecting semantic types like emails, and identifying categorical values.

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