gilfoyle

Analyze logs, metrics, and traces to pinpoint incident root causes.

216|10|Updated Jan 25, 2026

Search Tags:#automation #investigation #logs #observability #SRE #root-cause #incident-response

Ready to run

npx skills add https://github.com/axiomhq/gilfoyle --skill gilfoyle

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gilfoyle
Source: https://github.com/axiomhq/gilfoyle/tree/main
Command: npx skills add https://github.com/axiomhq/gilfoyle --skill gilfoyle

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Gilfoyle provides a data-driven approach to diagnosing root causes of incidents by querying and correlating observability data across logs, metrics, and traces, enabling faster restoration and learning.

Core Features & Use Cases

Hypothesis-driven RCA: Systematically test hypotheses using deterministic queries against real data.
Memory-backed insights: Persist verified facts, patterns, and queries to accelerate future incidents.
Unified tooling: Coordinate logs, metrics, and collaboration workflows to streamline incident response.

Quick Start

Use Gilfoyle to kick off an investigation by initializing memory and running a few queries to identify failing components.

Frequently Asked Questions about gilfoyle

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

FAQPage Schema

How do I pinpoint the root cause of an incident using observability data?▼

To pinpoint incident root causes from observability data, you apply hypothesis-driven queries across logs, metrics, and traces to systematically test and verify deterministic facts against your real system data.

What is the best way to automate root-cause investigation during incident response?▼

Automating root-cause investigation during incident response involves running deterministic queries against observability data to test hypotheses, then persisting verified facts to memory to accelerate future debugging.

Can I use this SRE investigation skill with my existing logs, metrics, and traces?▼

Yes, this SRE investigation skill correlates existing logs, metrics, and traces by running unified deterministic queries across your observability data to identify failing components during incidents.

How do I start an SRE investigation to identify failing components?▼

You start an SRE investigation by initializing memory and running a few deterministic queries against your observability data to systematically test hypotheses and identify failing components.

Does root-cause analysis memory persist between incident response investigations?▼

Yes, root-cause analysis memory persists verified facts, patterns, and queries between incident response investigations, enabling faster restoration and accelerated learning for future incidents.