backend-diagnose-workflow

Diagnose backend issues by collecting evidence, validating hypotheses, and deriving solutions.

1|Updated Nov 28, 2025

Search Tags:#workflow automation #troubleshooting #root cause analysis #backend #diagnosis #incident response

Ready to run

npx skills add https://github.com/ssaattww/ExcelReport --skill backend-diagnose-workflow

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: backend-diagnose-workflow
Source: https://github.com/ssaattww/ExcelReport/tree/main/.claude/skills/backend-diagnose-workflow
Command: npx skills add https://github.com/ssaattww/ExcelReport --skill backend-diagnose-workflow

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates the process of diagnosing backend issues, providing root-cause-oriented recommendations with clear confidence levels, eliminating the need for separate investigator, verifier, and solver subagents.

Core Features & Use Cases

End-to-End Diagnosis: Manages the entire diagnostic workflow from evidence collection to solution derivation within a single agent.
Root-Cause Analysis: Focuses on identifying the fundamental causes of backend failures, not just symptoms.
Confidence-Based Recommendations: Provides actionable solutions with explicit confidence scores, guiding implementation decisions.
Use Case: When a critical backend service experiences intermittent failures, this Skill can automatically collect logs, analyze traces, form hypotheses, validate them, and propose a fix with a confidence rating.

Quick Start

Use the backend-diagnose-workflow skill to diagnose the recent API latency spikes.

Frequently Asked Questions about backend-diagnose-workflow

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

FAQPage Schema

How do I automate root cause analysis for backend incidents without managing multiple subagents?▼

Automating root cause analysis within a single agent eliminates the need for separate investigator, verifier, or solver subagents. The workflow manages evidence collection, hypothesis validation, and solution derivation end-to-end, providing confidence-controlled recommendations for backend incidents.

What is the best way to diagnose intermittent backend service failures?▼

Diagnosing intermittent backend failures requires structured evidence collection and hypothesis validation. This workflow automatically gathers context, builds hypotheses, validates them against evidence, and derives actionable solutions with explicit confidence scores to guide implementation decisions.

How do I structure backend troubleshooting to focus on root causes instead of symptoms?▼

Structuring backend troubleshooting involves categorizing problem types and systematically validating hypotheses against collected evidence. This workflow focuses on identifying fundamental causes of failures rather than treating symptoms, recording residual risks alongside confidence-based solution recommendations.

Can I get confidence scores for backend diagnosis recommendations before implementing a fix?▼

Getting confidence scores for backend diagnosis recommendations is a core feature of this workflow. It provides actionable solutions with explicit confidence levels, ensuring implementation decisions are guided by validated evidence rather than speculation, which is critical for incident response.

What does an end-to-end backend issue diagnosis workflow include?▼

An end-to-end backend issue diagnosis workflow includes evidence collection, context gathering, hypothesis building, validation, solution derivation, and residual risk recording. It operates within a single agent to streamline incident response and deliver confidence-based recommendations.

When should I use an automated backend diagnosis workflow instead of manual investigation?▼

Use an automated backend diagnosis workflow when critical services experience failures requiring rapid root cause analysis. It replaces manual evidence gathering and hypothesis testing with a structured, confidence-controlled process, reducing incident response time and eliminating subagent overhead.