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query-patterns

Guide Honeycomb query construction and interpretation for trace and event datasets.

20|7|Updated Feb 7, 2026
Search Tags:#performance#debugging#data analysis#observability#honeycomb#querying
One-click install
npx skills add https://github.com/honeycombio/agent-skill --skill query-patterns-honeycombio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: query-patterns
Source: https://github.com/honeycombio/agent-skill/tree/main/honeycomb/skills/query-patterns
Command: npx skills add https://github.com/honeycombio/agent-skill --skill query-patterns-honeycombio

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides expert guidance on constructing and interpreting Honeycomb queries, ensuring you get the most accurate and actionable insights from your trace and event data.

Core Features & Use Cases

  • Optimized Query Construction: Learn best practices for selecting operations (e.g., P99 over AVG for latency), using relational fields, and creating calculated fields.
  • Effective Result Interpretation: Understand how to read query results, including raw JSON, ASCII heatmaps, and statistical heuristics.
  • Use Case: When investigating slow requests, this skill helps you choose the right query to identify tail latency issues and understand their root causes using relational fields.

Quick Start

Use the query-patterns skill to get guidance on how to find slow requests in Honeycomb.

Frequently Asked Questions about query-patterns

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

FAQPage Schema
How do I write a Honeycomb query to find slow requests and tail latency?▼

To find slow requests in Honeycomb, use the query-patterns guidance to select the P99 operation over AVG for latency, and apply relational fields to trace the root causes of tail latency in your event data.

What is the best way to query trace data for error rates in Honeycomb?▼

Querying trace data for error rates involves selecting the appropriate operation and using calculated fields. The query-patterns approach provides heuristics to construct these queries and interpret the statistical results accurately.

Can I use calculated fields and query math when analyzing event datasets in Honeycomb?▼

Yes, you can use calculated fields and query math on event datasets. This Skill provides opinionated guidance on constructing these calculations and interpreting the raw JSON or ASCII heatmap outputs for deep observability insights.

Does the query-patterns Skill support querying metrics datasets in Honeycomb?▼

No, the query-patterns Skill does not support metrics datasets. It provides opinionated querying guidance specifically for trace, span, and log or event datasets to help you investigate latency, errors, and outliers.

How do I interpret raw JSON and ASCII heatmap results from Honeycomb queries?▼

Interpreting raw JSON and ASCII heatmap results requires understanding statistical heuristics. The query-patterns guidance helps you read these outputs effectively to ensure you get actionable insights from your trace and event data.

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