Results for “debugging”

Expert knowledge of Folketrygdloven (National Insurance Act) processing in melosys-api. Use when: (1) Understanding FTRL bestemmelser (§2-5 to §2-13), (2) Debugging yrkesaktiv/ikke-yrkesaktiv/pensjonist processing flows, (3) Understanding trygdedekning options and combinations, (4) Investigating vilkår for each bestemmelse, (5) Understanding medlemskapsperiode generation logic, (6) Debugging frivillig vs pliktig membership determination.

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Bench instruments and measurement techniques for electronics — digital multimeter, oscilloscope, logic analyzer, spectrum analyzer, function generator, power supply, and their proper use. Covers probing technique, ground loops, loading effects, trigger modes, FFT mode pitfalls, signal integrity, noise budget analysis, and debugging workflows from "it doesn't work" to "here is the failure mechanism." Use when measuring, debugging, or characterizing any electronic circuit or firmware-hardware interaction.

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Use when the user reports a bug they can't reproduce, asks where to start debugging, or mentions a Heisenbug / production-only failure. Drives the observe→hypothesize→predict→test→iterate scientific method.

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OpenStack networking debug operations skill for SDN troubleshooting, packet tracing, and flow analysis. Covers OVS/OVN debugging (ovs-vsctl, ovs-ofctl, ovs-appctl, ovn-nbctl, ovn-sbctl, ovn-trace), security group analysis via OVS flow rules and conntrack, DHCP troubleshooting through namespace inspection and dnsmasq diagnostics, floating IP diagnosis with NAT rule and ARP verification, network namespace inspection (ip netns), MTU chain analysis for overlay networks, DNS resolution debugging, and east-west traffic diagnosis. Use when diagnosing network connectivity failures, tracing packets through the SDN stack, or analyzing flow tables in a running OpenStack cloud.

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Interactive assistant for intercepting, debugging, analyzing and reviewing Claude Code API requests using mitmproxy. Guides setup, certificate configuration, and active traffic inspection via web or CLI interface. Supports learning Claude Code internals, debugging issues, and optimizing API usage.

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Use when debugging software, investigating incidents, diagnosing flaky tests, or analyzing performance regressions — enforces structured observation recording with evidence IDs, causality validation, and verification gates to prevent correlation-causation pollution. Use when an agent might otherwise summarize or speculate instead of reporting observed evidence.

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Delegates coding tasks to Codex CLI for prototyping, debugging, and code review. Use when: (1) Backend/logic implementation, (2) Algorithm design and optimization, (3) Bug analysis and debugging, (4) API/database code generation, (5) Code quality review and refactoring. Triggers: "implement algorithm", "debug", "analyze code", "backend task", "API implementation", "optimize performance", "refactor", "generate prototype", "code review". IMPORTANT: Default sandbox="read-only" (analysis/review/patch generation); use sandbox="workspace-write" only when codex must run tests/builds to verify. Always request unified diff patches only. Supports multi-turn sessions via SESSION_ID.

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[AUTO-INVOKE] MUST be invoked when debugging failed on-chain transactions. Covers transaction receipt analysis, gas diagnosis, calldata decoding, revert reason extraction, and state verification. Trigger: any task involving failed tx analysis, revert debugging, or on-chain transaction troubleshooting.

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Debug CI failures in unfamiliar repo pipelines. Reads CI configs, fetches failure logs, classifies the failure type, and guides the user through diagnosing and fixing the issue. Use when CI fails on your PR in an OSS repo, when you don't understand a CI error, or when debugging GitHub Actions/CircleCI/other CI systems. Not for setting up CI from scratch — this is for debugging existing pipelines.

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What this project has already learned the hard way, and where any new such fact belongs. Two jobs. (1) Routing: this project keeps NO per-project Claude memories — read this before writing anything down, and pick a durable home from the list here. (2) An index, by area, of non-obvious facts worth knowing before you guess: debugging rigs and measurements that lie, iOS/WebKit and the tethered iPhone, the Windows app, production and deployment, and general codebase traps. Use before setting up a browser- or server-driven debugging session, when a bug reproduces only on iPhone, in Safari or in the Windows app, before changing deployment configuration or reading store/crash data, when something behaves unexpectedly and you are about to guess why, and whenever you are tempted to record a fact for later.

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Use for debugging and system comprehension. Triggers: debug this, root cause, how does X work, reverse engineer, 왜 안 돼, 디버깅, 원인 분석, 로직 파악, 뜯어봐.

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Systematic root-cause isolation and minimal fix proposal for software bugs.

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Systematic debugging methods, log analysis, and performance diagnostics. Use when debugging issues, analyzing errors, or troubleshooting incidents.

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Systematic, hypothesis-driven debugging workflow with triage-based track routing. Use for debugging, fixing bugs, and investigating errors.

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Systematic, hypothesis-driven debugging workflow with triage-based track routing. Use when asked to "fix this bug", "debug this", "why is this failing", "this is broken", "investigate this error", "track down this issue", or any debugging situation. Supports --deep flag to force full investigation.

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

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Guide a person in systematic observation of systems, patterns, or phenomena. AI coaches neutral attention, field notes methodology, pattern recognition, hypothesis formation, and structured reporting for debugging, research, and system understanding. Use when a person wants to understand a system's behavior before intervening, when someone keeps jumping to conclusions and needs the discipline of observation first, when preparing an evidence-based report, or when studying team dynamics or process effectiveness through direct observation.

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AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis. Implements Cursor Debug Mode methodology — structured hypothesis ranking, targeted code instrumentation, human-in-the-loop reproduction gate, log-confirmed root cause, and mandatory cleanup.

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Systematic 4-phase debugging with root cause investigation. Use when fixing bugs to prevent random fixes.

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Use when debugging with LLDB on macOS, FreeBSD, or Linux-clang, mapping GDB commands to LLDB, Xcode or VS Code integration, LLDB Python scripting, or debugging Swift and Objective-C.

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Use when debugging CUDA with cuda-gdb or Compute Sanitizer, reading GPU core dumps, using device printf, or triaging error codes 700, 701, 702, and 719. Not for performance: use cuda-profiling.

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Use when debugging the Linux kernel: kgdb and kdb, ftrace and kprobes, dynamic debug, kdump and crash analysis, or printk levels on a live or crashed target.

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Use when choosing kernel spinlocks versus mutexes, using RCU, seqlocks, completions, or memory barriers, or debugging scheduling-while-atomic. Not for userspace atomics: use concurrency-debugging.

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Use when writing CUDA kernels, managing the thread, block, and grid hierarchy, tiling shared memory, using streams, setting nvcc flags, or using Thrust. Not for kernel debugging: use cuda-debugging.

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