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Found 30 Skills
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Debug web and Node.js applications using console inspection, network analysis, and non-blocking code debugging. Use when the user reports bugs, wants to debug JavaScript (browser or backend), inspect network requests, troubleshoot page/API issues, trace function calls, or monitor exceptions.
Use when working with error diagnostics smart debug
Structured debugging session — reproduce, isolate, diagnose, and fix. Trigger with an error message or stack trace, "this works in staging but not prod", "something broke after the deploy", or when behavior diverges from expected and the cause isn't obvious.
Manages Android performance profiling and debugging. Triggers when the user asks to record or analyze Android performance data, such as system traces, heap dumps, method recordings, callstack samples, memory allocations, or investigate bottlenecks, jank, memory leaks, and app startup issues on Android, or when the user asks to write, debug, or execute ad-hoc SQL queries. Applies to both user and system apps or services.
This skill should be used when user asks about "GCloud logs", "Cloud Logging queries", "Google Cloud metrics", "GCP observability", "trace analysis", or "debugging production issues on GCP".
Intelligent Core Web Vitals analysis with automated workflows and decision trees. Measures LCP, CLS, INP with guided debugging that automatically determines follow-up analysis based on results. Includes workflows for LCP deep dive (5 phases), CLS investigation (loading vs interaction), INP debugging (latency breakdown + attribution), and cross-skill integration with loading, interaction, and media skills. Use when the user asks about Core Web Vitals, LCP optimization, layout shifts, or interaction responsiveness. Compatible with Chrome DevTools MCP.
This skill should be used when debugging full-stack issues that span UI, backend, and database layers. It provides a systematic workflow to detect errors, analyze root causes, apply fixes iteratively, and verify solutions through automated server restarts and browser-based testing. Ideal for scenarios like failing schedulers, import errors, database issues, or API payload problems where issues originate in backend code but manifest in the UI.
Systematically diagnose and resolve bugs through conversational investigation and root cause analysis
Full debugging workflow — reproduce the bug with a failing test, perform root cause analysis, then implement a minimal fix.
Systematic root-cause debugging: reproduce, investigate, hypothesize, fix with verification. Use when asked to "debug this", "fix this bug", "why is this failing", "troubleshoot", or mentions errors, stack traces, broken tests, flaky tests, regressions, or unexpected behavior.