Skill UI

Analyzes Antigravity AI coding sessions to classify scope deltas, rework patterns, root causes, and recommend prompt or process improvements backed by evidence.

Structured facilitation of post-incident reviews that captures timelines, successes, failures, root causes, response metrics, and actionable improvements to strengthen future incident response.

This skill provides a comprehensive audit workflow for diagnosing performance issues in SwiftUI applications. It begins with a code-first review, identifying common performance smells like invalidation storms or layout thrash. If code inspection is insufficient, it guides the user through collecting runtime profiling evidence (e.g., Instruments data) to pinpoint root causes such as janky scrolling, high CPU usage, or memory leaks.

This skill enforces a rigorous, 5-phase debugging protocol (Reproduce, Isolate, Root Cause, Fix, Verify) for AI coding agents. It forces a disciplined approach, preventing agents from guessing fixes or making premature edits. Use this when debugging complex, intermittent, or hard-to-isolate bugs to ensure the root cause is fully confirmed before any code changes are applied, maximizing debugging accuracy.

Structured workflow that has an AI agent capture failures, diagnose root causes, apply minimal recovery steps, and report introspection to avoid repeat loops or context drift.

A step-by-step debugging guide that starts by stopping feature work and preserving evidence, then moves through triage (reproduce, localize, reduce), root-cause fixes, recurrence guards, and verification to handle test/build/runtime failures and incidents without guessing.

Structured Go debugging workflow that guides reproducing bugs, tracing root causes, and leveraging tools like tests, pprof, Delve, and race detector when programs crash, hang, or behave unexpectedly.

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 advanced workflow automates the process of analyzing the operational health of specific Azure resources. It ingests and analyzes raw logs, telemetry data, and performance metrics from diverse Azure services (e.g., VMs, Web Apps, Databases). The system performs comprehensive root cause analysis, classifies issues (Critical, High, Medium), and generates actionable, step-by-step remediation plans, significantly reducing manual troubleshooting time.

This guide outlines the process and mandatory structure for documenting defects found during an Oracle-to-PostgreSQL migration. It details how to write actionable bug reports, covering required elements like Severity, Root Cause, Scenario, and Solution. It emphasizes treating Oracle as the source of truth and handling data layer nuances (e.g., NULL vs. empty string) to ensure accurate defect tracking and remediation.

This skill provides advanced, deep-dive debugging capabilities for failing Power Automate cloud flows using the FlowStudio MCP server. Unlike standard API calls that only show top-level status codes, this tool retrieves action-level inputs, outputs, and detailed error information. Use it to find the true root cause of complex failures, diagnose expression errors, check connector authentication issues, or trace intricate flow execution paths.