Skill UI

This structured prompt guides an AI model to proactively analyze a user's question and systematically list all necessary files for accurate answering. It forces the AI to categorize required context into 'Must See' (critical), 'Should See' (helpful), 'Already Have,' and 'Uncertainties.' Ideal for complex coding tasks, debugging, or feature implementation that spans multiple codebase files.

A comprehensive, four-stage methodology for debugging complex software issues, ensuring that fixes target the root cause rather than merely masking symptoms. This guide mandates rigorous investigation (gathering evidence, tracing data flow), pattern analysis, controlled hypothesis testing, and disciplined implementation via test-driven development. Use this when facing production bugs, test failures, performance degradation, or unexplained system behavior, especially under time pressure.

This technique is used when multiple failure points (e.g., different test files or subsystems) are discovered, and these failures are independent of each other. Instead of debugging them sequentially, you dispatch separate, focused AI agents to work on each problem domain concurrently. This maximizes efficiency by allowing parallel investigation, dramatically reducing the time spent on large-scale debugging tasks.

WizTelemetry Logging is a comprehensive extension component within the KubeSphere Observability Platform. It provides robust capabilities for collecting, processing, and storing logs from container environments (stdout/stderr) and local disk files. Designed for system monitoring, it integrates with OpenSearch and supports advanced features like log query APIs, making it essential for maintaining system visibility and debugging in Kubernetes clusters.