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filtering

Filter data across perspective queries, metric aggregations, and attribute expressions.

39|1|Updated Feb 23, 2026
Search Tags:#metrics#data analysis#filtering#sql#data manipulation#querying
One-click install
npx skills add https://github.com/honeydew-ai/honeydew-ai-coding-agents-plugins --skill filtering
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: filtering
Source: https://github.com/honeydew-ai/honeydew-ai-coding-agents-plugins/tree/main/plugins/data-analysis-tools/skills/filtering
Command: npx skills add https://github.com/honeydew-ai/honeydew-ai-coding-agents-plugins --skill filtering

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill enables precise data filtering across various contexts, ensuring you analyze only the relevant subsets of your data.

Core Features & Use Cases

  • Flexible Filtering Syntax: Supports comparisons, string matching, NULL checks, booleans, and date handling.
  • Multiple Contexts: Apply filters in perspective queries (pre-aggregation), metric aggregations, attribute expressions, and metric value filtering (post-aggregation).
  • Use Case: Filter a customer dataset to include only active users from the last quarter who made a purchase over $100.

Quick Start

Filter the detailed_listings data to include only listings where the room_type is 'Entire home/apt' and the price is greater than 50.

Frequently Asked Questions about filtering

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

FAQPage Schema
How do I apply multiple conditions when filtering data for analysis?▼

Data filtering with multiple conditions uses boolean logic, comparison operators, and string matching to isolate relevant subsets. This ensures you analyze only the data that meets your complex criteria across various analytical contexts.

What is the difference between pre-aggregation and post-aggregation filtering?▼

Pre-aggregation filtering applies conditions to raw data before metric calculations, while post-aggregation filtering evaluates aggregated metric values. Both contexts are supported to give you precise control over your data analysis pipeline.

Can I use SQL-style querying and NULL checks to filter my dataset?▼

Yes, SQL-style querying and data manipulation are supported, including specific syntax for NULL checks and attribute expressions. You can perform precise data filtering across perspective queries and metric aggregations.

How do I filter data based on date criteria and string manipulation?▼

Filtering data by date criteria and string manipulation uses specific syntax for string matching and date-based conditions. This allows you to isolate relevant records, such as active users from the last quarter, before running metric aggregations.

Does this data filtering approach support complex metric aggregations and attribute expressions?▼

Yes, the data filtering approach supports metric aggregations and attribute expressions by applying specific syntax across multiple contexts. You can filter perspective queries and apply boolean logic to ensure accurate metric calculations.

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