investigate-app

Investigate production applications using Kubernetes logs and Jaeger traces.

1|Updated Jun 3, 2026

Search Tags:#optimization #security audit #performance tuning #production analysis #application debugging

Ready to run

npx skills add https://github.com/SchSeba/ai-plugins --skill investigate-app

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: investigate-app
Source: https://github.com/SchSeba/ai-plugins/tree/main/skills/investigate-app
Command: npx skills add https://github.com/SchSeba/ai-plugins --skill investigate-app

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill unit assists with the comprehensive analysis of a running production application to identify bugs, performance issues, and other challenges, providing a structured plan for optimization.

Core Features & Use Cases

Log and Trace Analysis: Combines pod logs and Jaeger traces for multi-dimensional reviews.
Specialist Sub-agents: Engages in parallel analysis for security, stability, performance, and more.
Actionable Improvement Plan: Offers prioritized suggestions with effort estimates and potential risks.
Memory and Prior Findings: Incorporates knowledge from previous investigations for more accurate and informed analysis.

Quick Start

Use the /investigate-app command to start an analysis of a production application by providing kubectl log commands and optionally a Jaeger endpoint and service list.

Frequently Asked Questions about investigate-app

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

FAQPage Schema

How do I perform a production application analysis using Kubernetes logs and Jaeger traces?▼

Production application analysis combines Kubernetes pod logs and Jaeger traces for multi-dimensional review. Automated parallel specialist sub-agents evaluate security, stability, and performance, generating an actionable, prioritized improvement plan with effort estimates and risks.

What is the best way to create an actionable improvement plan for production application debugging?▼

An actionable improvement plan is generated by analyzing collected pod logs and tracing data. The system leverages a memory database of prior findings to provide prioritized optimization suggestions, complete with estimated effort and potential risks for production application debugging.

Can I use Jaeger tracing and kubectl logs for comprehensive security audit and performance tuning?▼

Yes, Jaeger tracing and kubectl logs serve as primary inputs for security audit and performance tuning. The analysis engages specialist sub-agents in parallel to evaluate these data sources, ensuring comprehensive coverage of application stability and security challenges.

How does incorporating memory of past findings improve production application optimization?▼

Incorporating memory of past findings improves production application optimization by maintaining a historical database. This allows the analysis to recall previous bugs and performance issues, resulting in more accurate, informed, and contextually aware improvement planning.

Do I need to provide a Jaeger endpoint to start an application debugging investigation?▼

No, providing a Jaeger endpoint is optional for application debugging. You can initiate the investigation by providing kubectl log commands, and optionally include a Jaeger endpoint and service list to enable deeper distributed tracing analysis.