field-failure-investigator

Structure post-failure analyses from field symptoms, logs, and hardware context.

1|Updated Mar 16, 2026

Search Tags:#investigation #logs #root-cause-analysis #incident-triage #field-failure #hardware-context #evidence-prioritization

Ready to run

npx skills add https://github.com/00PrabalK00/claude-skills --skill field-failure-investigator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: field-failure-investigator
Source: https://github.com/00PrabalK00/claude-skills/tree/main/skills/field-failure-investigator
Command: npx skills add https://github.com/00PrabalK00/claude-skills --skill field-failure-investigator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

An organized, repeatable approach to post-failure analysis that consolidates field symptoms, operator notes, logs, and hardware context into a clear diagnosis and actionable next steps.

Core Features & Use Cases

Structured evidence capture: collects and aligns symptoms, logs, configs, and context for rapid analysis.
Cause ranking & guardrails: prioritizes likely causes, separates symptoms from potential root causes, and flags uncertainty.
Actionable outputs: delivers concise diagnoses, supporting evidence, and the smallest safe fix path or experiment.

Quick Start

Structure a post-failure analysis using field symptoms, logs, operator notes, and hardware context.

Frequently Asked Questions about field-failure-investigator

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

FAQPage Schema

How do I structure post-failure analysis from field logs and hardware context?▼

Structure post-failure analysis by consolidating field symptoms, operator notes, logs, and hardware context into a clear diagnosis that ranks likely causes and proposes the smallest safe fix.

What is the best way to perform root-cause analysis for fielded systems outside lab conditions?▼

Root-cause analysis for fielded systems requires collecting high-signal evidence, separating symptoms from probable causes, and proposing a safe experiment to validate the explanation across diverse hardware configurations.

How do I separate symptoms from probable causes during incident triage?▼

Incident triage separates symptoms from probable causes by applying cause ranking guardrails that prioritize likely explanations, flag uncertainty, and align operator notes with log evidence.

Can I use this approach for field failures across diverse hardware and operating contexts?▼

Yes, this approach applies to fielded systems experiencing failures across diverse hardware, configurations, and operating contexts, enabling rapid triage and root-cause justification outside lab conditions.

How do I prioritize likely causes when investigating field failures with limited log data?▼

Prioritize likely causes by collecting high-signal evidence from available logs and hardware context, then ranking explanations while explicitly flagging uncertainty in the diagnosis output.

What steps do I follow to validate a root cause hypothesis for a field failure?▼

Validate a root cause hypothesis by proposing the smallest safe fix or experiment that confirms the explanation, supported by aligned evidence from symptoms, logs, and hardware context.