Results for “debugging”

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

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Use when seeing WebSocket errors like "Invalid frame header", "RSV1 must be clear", or "WS_ERR_UNEXPECTED_RSV_1" - covers multiple WebSocketServer conflicts, compression issues, and raw frame debugging techniques

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Plan, implement, or review systematic debugging work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests systematic debugging work.

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Testing and debugging for Zephyr RTOS. Covers the Ztest framework for unit and integration tests, the Twister test runner for automated HIL/SIM validation, and advanced tracing/debugging techniques (SystemView, Thread Analyzer, Stack analysis). Trigger when writing unit tests, setting up CI/CD pipelines, or analyzing system performance and latency.

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Use when running GDB: breakpoints, watchpoints, segfault or hang debugging, reverse debugging, remote gdbserver, core dumps, pretty-printers, Python scripting, or multi-threaded debugging.

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This skill provides expert-level guidance for debugging and fixing bugs in VS Code extensions. Use when investigating runtime errors, fixing memory leaks, resolving WebView issues, debugging activation problems, fixing TypeScript type errors, or troubleshooting extension communication failures. Covers systematic debugging workflows, common bug patterns, root cause analysis, and prevention strategies.

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Systematic 4-phase debugging framework for Unity projects with GameDebug wrapper generation. Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes. Triggers on requests like "this doesn't work", "there's a bug", "it crashes when", "fix this error", "why is this happening", "null reference exception", "test is failing", "unexpected behavior", "something broke", or any debugging task. Four-phase framework (gather evidence, isolate, hypothesize, fix) that ensures understanding before attempting solutions.

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4-phase systematic debugging methodology with root cause analysis and evidence-based verification. Use when debugging complex issues.

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Time-travel debugging for DAG executions. Inspect agent state at any node, replay decisions with modified inputs, compare execution traces side-by-side, and identify where reasoning diverged. Inspired by LangGraph Studio's state-editing model and Temporal's event history. Activate on "debug DAG", "replay execution", "time travel debug", "inspect node state", "what went wrong at step", "compare runs", "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.

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Four-phase debugging framework that ensures root cause investigation before attempting fixes. Never jump to solutions.

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Systematic debugging: production-level reproduce, isolate, instrument, verify. Use when implementing, reviewing or testing systematic debugging for React, Vite, Next.js or Perfect_AI projects.

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Apply systematic root cause analysis and debugging methodologies to diagnose and fix bugs, test failures, and unexpected behavior. Use when encountering production issues, investigating test failures, diagnosing performance problems, tracing error sources through call stacks, analyzing logs and stack traces, reproducing inconsistent bugs, debugging race conditions, investigating memory leaks, or applying scientific method to problem-solving before proposing fixes.

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4-phase root cause debugging: understand bugs before fixing.

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[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and resolve production issues. The intelligent debugging strategy combines automated root cause analysis with human expertise, using modern 2024/2025 practices including AI code assistants (GitHub Copilot, Claude Code), observability platforms (Sentry, DataDog, OpenTelemetry), git bisect automation for ...

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You are a debugging expert specializing in setting up comprehensive debugging environments, distributed tracing, and diagnostic tools. Configure debugging workflows, implement tracing solutions, and establish troubleshooting practices for development and production environments.

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Expert DevOps troubleshooter specializing in rapid incident response, advanced debugging, and modern observability. Masters log analysis, distributed tracing, Kubernetes debugging, performance optimization, and root cause analysis. Handles production outages, system reliability, and preventive monitoring. Use PROACTIVELY for debugging, incident response, or system troubleshooting.

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Investigate bugs, failing tests, and unexpected behavior using reproduction steps, logs, and recent changes. Follow a structured process to identify the root cause, test a specific explanation, and verify the resulting fix.

• Investigating failing tests
• Tracing the cause of a production bug
• Finding where a build or integration breaks

Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.

• Diagnosing repeated failures in an agent run
• Investigating loops through the same tool calls
• Reviewing output degradation as context grows

Build and maintain MCP servers that let AI assistants use tools, read data, and access reusable prompts. Covers Node/TypeScript implementation, input validation, transport choices for local or remote clients, and registration or connection troubleshooting.

• Building an MCP server for AI assistants
• Adding tools, resources, and reusable prompts
• Choosing local or remote connection methods

Create and review Kubernetes configuration files with examples for health checks, access permissions, resource limits, and automatic scaling. Use the included kubectl commands to investigate container crashes, memory shortages, and deployment problems.

• Create Kubernetes deployment configuration
• Review health checks and resource limits
• Investigate repeatedly crashing containers

Build and review Nuxt 4 applications with consistent server and browser rendering. Guide data fetching, page rendering and caching rules, and deferred loading to address rendering mismatches and improve performance.

• Debugging differences between server and browser rendering
• Reviewing page data fetching with useFetch and useAsyncData
• Choosing rendering and caching rules for each route group

Guide development and debugging in Next.js 16 and later using Turbopack, a tool that bundles application code. Review slow startup, live updates, caching, webpack alternatives, bundle analysis, and version-specific proxy file naming.

• Investigating slow development server startup
• Checking delays in live updates after code changes
• Choosing between Turbopack and webpack

Implement asynchronous work and changing data streams in Android and Kotlin Multiplatform apps using Kotlin Coroutines and Flow. Apply patterns for parallel loading, UI state updates, cancellation, error handling, and tests that check emitted values.

• Loading several data sources in parallel
• Managing screen state with StateFlow
• Handling search input delays and network retries

Diagnose failed AI agent runs by capturing errors, checking likely causes, and choosing small, reversible recovery actions. The workflow produces a structured report of the failure, recovery steps, and evidence, helping explain repeated retries or stalled progress.

• Diagnosing repeated tool calls with no progress
• Investigating quality loss as context grows
• Checking file paths and environment mismatches