What is the Everyrow SDK?

Everyrow SDK is a Python library that uses AI agents to perform intelligent operations like ranking, merging, and deduping on pandas DataFrames based on natural language instructions.

Does it work with standard pandas DataFrames?

Yes, Everyrow operations take standard pandas DataFrames as input and return results that include processed DataFrames, making it compatible with existing data science pipelines.

How does Everyrow handle deduplication differently than standard tools?

Unlike traditional exact-match or fuzzy-logic tools, Everyrow uses semantic understanding to identify that different strings (e.g., 'AbbVie' and 'AbbVie Pharmaceutical') represent the same legal entity.

Does this skill require an API key?

Yes, Everyrow operations require an EVERYROW_API_KEY. The skill includes logic to help you configure this environment variable if it is missing.

Can I use Everyrow for complex research-based filtering?

Yes, the 'screen' operation allows you to filter rows based on research-intensive criteria that require AI reasoning, such as evaluating business models or sensitivity to geopolitical tensions.

Everyrow AI Data Processing

Name: Everyrow AI Data Processing
Author: futuresearch

byfuturesearch

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データサイエンスとML

Integrates AI-powered qualitative analysis into Python pandas workflows for intelligent data sorting, deduplication, and merging.

概要

The Everyrow SDK skill empowers Claude to perform complex data manipulations that traditional deterministic code struggles with, such as ranking rows by subjective criteria or merging datasets with mismatched keys. By leveraging AI agents, it enables semantic deduplication, research-intensive filtering, and batch processing of natural language tasks across pandas DataFrames. This skill is an essential tool for data scientists and developers who need to bridge the gap between unstructured reasoning and structured tabular data directly within their Python environment.

主な機能

AI-powered table merging for datasets with inconsistent primary keys
Qualitative ranking and scoring of rows using natural language criteria
10 GitHub stars
Semantic deduplication that understands entity equivalence beyond exact matches
Research-driven screening and filtering using Pydantic-validated models
Async support and session management for long-running batch data operations

ユースケース

Cleaning CRM data by merging duplicate company entries with varying naming conventions
Filtering stock indices based on complex socio-economic criteria or market exposure
Scoring sales leads based on their qualitative likelihood to need specific technology solutions

概要

主な機能

AI-powered table merging for datasets with inconsistent primary keys
Qualitative ranking and scoring of rows using natural language criteria
10 GitHub stars
Semantic deduplication that understands entity equivalence beyond exact matches
Research-driven screening and filtering using Pydantic-validated models
Async support and session management for long-running batch data operations

ユースケース

Cleaning CRM data by merging duplicate company entries with varying naming conventions
Filtering stock indices based on complex socio-economic criteria or market exposure
Scoring sales leads based on their qualitative likelihood to need specific technology solutions