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honeycomb-analysis

Run statistics, queries, and dataset discovery on Honeycomb datasets.

654|77|Updated Jan 20, 2026
Search Tags:#queries#observability#statistics#datasets#dataset-discovery#honeycomb
One-click install
npx skills add https://github.com/incidentfox/incidentfox --skill honeycomb-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: honeycomb-analysis
Source: https://github.com/incidentfox/incidentfox/tree/main/sre-agent/.claude/skills/observability-honeycomb
Command: npx skills add https://github.com/incidentfox/incidentfox --skill honeycomb-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires httpx, and includes scripts (resource) components.

What problem does it solve?

Observability analysis can be time-consuming when manually querying across Honeycomb datasets. This skill provides ready-to-run scripts and utilities to surface actionable insights quickly, accelerating incident investigation and performance optimization.

Core Features & Use Cases

  • Prebuilt scripts: get_statistics.py, run_query.py, list_datasets.py to surface counts, breakdowns, and custom metrics.
  • Dataset discovery and exploration: list_datasets.py returns available Honeycomb datasets with metadata.
  • SLOs and triggers: list_slos.py and list_triggers exposed via run_query.py for monitoring and alerting workflows.
  • Use Case: An on-call engineer runs get_statistics.py on production to estimate error rate and top endpoints, then drills into P99 latency by service with run_query.py.

Quick Start

Install Python 3, install httpx, clone the repository, and run:

  • python scripts/get_statistics.py production
  • python scripts/run_query.py production --calc P99 --breakdown service.name --column duration_ms
  • python scripts/list_datasets.py

Frequently Asked Questions about honeycomb-analysis

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

FAQPage Schema
How do I query Honeycomb datasets to analyze observability data?▼

You can query Honeycomb datasets to analyze observability data using prebuilt Python scripts like run_query.py. It allows you to calculate custom metrics such as P99 latency and group results by specific columns like service name.

How do I list available datasets in Honeycomb for incident investigation?▼

To list available datasets in Honeycomb for incident investigation, you run the list_datasets.py script. It returns all accessible datasets along with their associated metadata to help you locate the correct data.

How do I get statistics from Honeycomb traces to estimate error rates?▼

To get statistics from Honeycomb traces to estimate error rates, you run the get_statistics.py script against your target dataset. It surfaces counts and breakdowns to help you quickly identify top endpoints and issues.

What's the best way to check Honeycomb SLOs and triggers for monitoring?▼

The best way to check Honeycomb SLOs and triggers for monitoring is by executing the list_slos and list_triggers commands. These are exposed through the run_query.py script to support your alerting workflows.

Does this Honeycomb analysis script require Python and httpx?▼

Yes, this Honeycomb analysis script requires Python 3 and the httpx library. You also need the internal honeycomb_client module configured in your environment to execute the statistics and query scripts successfully.

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