root-cause-localization

Name: root-cause-localization
Availability: InStock
Author: linfordwu

Identify root-cause components from logs and traces within a fault window.

1|1|Updated Apr 25, 2026

Search Tags:#logs #traces #root-cause #dependency-graph #rca #fault-diagnosis #time-alignment

Ready to run

npx skills add https://github.com/linfordWu/owls --skill root-cause-localization

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: root-cause-localization
Source: https://github.com/linfordWu/owls/tree/main/skills/root-cause-localization
Command: npx skills add https://github.com/linfordWu/owls --skill root-cause-localization

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, drain3, scikit-learn, networkx, pytz, and includes scripts (resource) components.

What problem does it solve?

Root-cause localization from logs and traces is essential to reduce MTTR by accurately identifying the true initiators of failures within a defined time window, using evidence from multiple sources.

Core Features & Use Cases

Time-aligned analysis of logs and traces to converge on root-cause components, with support for multiple root-causes when necessary.
Dependency and call-chain reasoning to trace propagation paths and distinguish victims from triggers.
Structured root-cause outputs that can feed upstream orchestrators and RCA coordination workflows.

Quick Start

Provide a fault window and candidate components to obtain a ranked root-cause list.

Frequently Asked Questions about root-cause-localization

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

FAQPage Schema

How do I identify the root cause of a failure from logs and traces within a specific time window?▼

Root-cause localization from logs and traces analyzes time alignment and call chains within a fault window to identify true failure initiators. It distinguishes victims from triggers by reasoning about dependency graphs and failure propagation paths.

How do I trace failure propagation paths using a dependency graph for fault diagnosis?▼

Trace failure propagation by analyzing dependency graphs and call chains to distinguish root-cause components from affected victims. This approach uses time-aligned diagnostic traces to converge on the true initiators of failures within your defined fault window.

What inputs do I need to provide for root-cause localization from diagnostic traces?▼

You need to provide a fault window, candidate components, and diagnostic traces as inputs. The analysis outputs structured root-cause items that can feed upstream orchestrators and RCA coordination workflows.

Can I use this root-cause analysis approach if there are multiple simultaneous failures in my system?▼

Yes, the root-cause localization supports identifying multiple root causes when necessary. It analyzes time-aligned logs and traces to converge on all true failure initiators within the given fault window, delivering structured root-cause entries for downstream orchestration.

Does this fault diagnosis tool require specific Python dependencies like pandas and networkx?▼

Yes, fault diagnosis with this tool requires Python dependencies including pandas, numpy, drain3, scikit-learn, networkx, and pytz. These libraries enable time-aligned analysis of logs and traces and support dependency graph reasoning for root-cause localization.

Why does time alignment matter when diagnosing root causes from logs and traces?▼

Time alignment matters because it allows the root-cause localization to accurately correlate events across logs and traces within a fault window. This temporal correlation is essential for tracing call chains and determining the true initiators of failures.