Skill UI

Guides senior quality engineers through the regulated non-conformance lifecycle, root cause analysis, CAPA execution, SPC insight, and supplier quality oversight to keep product safety and compliance intact.

This specialized skill guides users through a structured, forensic investigation process when facing sudden, significant drops in organic traffic or search rankings. It is designed for incident scenarios—such as suspected penalties, Core Updates, major technical regressions, or unexplained traffic crashes—providing a systematic approach to triage, root-cause analysis, and developing a prioritized recovery plan.

This comprehensive guide provides advanced troubleshooting workflows for complex, microservices-based environments running on Kubernetes. It covers diagnosing issues like CrashLoopBackOff, cascade failures, resource pressure (CPU/Memory), and detailed Redis state analysis, ensuring systematic root cause identification across multiple services.

A rigorous, blameless framework for conducting post-mortems after major failures, such as missed quarterly targets, failed launches, or poor hires. It guides users through defining the event, applying the 5 Whys to find root causes, distinguishing between contributing factors and core causes, identifying ignored warning signs, and creating actionable, owned change registers to ensure systemic improvement and prevent recurrence.

This module is designed to document, track, and manage quality nonconformance issues found during manufacturing or process audits. It is essential for root cause analysis, implementing corrective and preventive actions (CAPA), and ensuring compliance with industry quality standards. Use it when recording deviations from specified requirements.

A forensic tool for performing post-mortem analysis on AI-assisted coding sessions. It goes beyond simply stating what was done, determining *why* work deviated (e.g., spec ambiguity, repo friction, agent error), identifying scope creep, and classifying rework patterns. It provides evidence-backed recommendations to improve prompts, codebase health, and future development workflows.

A comprehensive guide for collecting volatile forensic evidence from a compromised host while adhering to the order of volatility. This process captures critical data—including memory dumps, network connections, running processes, and system state—before the host is isolated or powered down. Essential for root cause analysis, fileless malware investigation, and maintaining a defensible chain of custody for legal proceedings.

This comprehensive guide outlines a structured workflow for conducting digital forensics investigations across major cloud providers, including AWS, Azure, and GCP. It covers critical steps such as evidence preservation (snapshots, metadata capture), systematic log collection (CloudTrail, Activity Logs, Audit Logs, VPC Flow Logs), and advanced analysis techniques to trace unauthorized accesses and identify root causes of security breaches.

This skill provides a comprehensive, structured workflow for diagnosing performance bottlenecks in SwiftUI applications. It guides users through a code-first review to detect common issues like invalidation storms, layout thrash, or heavy main-thread work. When code review is insufficient, it provides detailed guidance on collecting runtime evidence using Instruments, enabling precise root cause analysis and targeted remediation strategies.

This skill teaches users to apply the scientific method to software debugging. Instead of random changes, it guides the process through five steps: Observation, Hypothesizing, Prediction, Testing, and Analysis. By systematically forming and testing specific, measurable hypotheses, users can efficiently isolate failing components and confirm the true root cause of technical issues.

Performs a systematic and structured investigation to determine the true source of bugs, errors, and unexpected system behavior, rather than merely treating symptoms. This method utilizes techniques like the Five Whys, hypothesis-driven testing, stack trace analysis, and gathering evidence (logs, git diffs) across multiple phases. It is ideal for complex, intermittent, or deeply rooted technical issues in software development.

This skill provides comprehensive tools to download, export, and deeply inspect existing Arize traces and spans. It is essential for understanding the full execution flow of complex LLM applications, debugging runtime errors, and performing root-cause analysis. Users can retrieve detailed data filtered by Trace ID, Span ID, or Session ID, enabling thorough analysis of conversational or multi-step agent behaviors.