unf
OfficialConsistent data fingerprinting with format-invariant hashing.
Authorcodata
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill provides a reliable mechanism for generating unique, format-invariant fingerprints for data objects, ensuring data integrity and facilitating reproducible analyses.
Core Features & Use Cases
- Semantic Hashing: Creates order-invariant fingerprints for strings and data columns, enabling consistent identification across formats.
- Vector and File Hashing: Supports dataframes and raw data files in CSV, Parquet, SAS, and Stata formats to produce reproducible identifiers.
- Use Case: Verify that datasets stored in different formats or with columns reordered produce the same identifier for version control and data validation.
Quick Start
Use the unf skill to generate a fingerprint for your dataset stored in "data.parquet" to verify data consistency across formats.
Dependency Matrix
Required Modules
polarsdartfx-unf
Components
scripts
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: unf Download link: https://github.com/codata/croissant-toolkit/archive/main.zip#unf Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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