「crash」の検索結果

Fix intermittent `{:no_translation, :unicode, :latin1}` crashes in Elixir escript daemons that use length-prefixed framed IPC on stdin/stdout. Use when: (1) daemon worker crashes only on some input files, usually ones with non-ASCII bytes (kanji, cyrillic, emoji); (2) error surfaces as `Protocol error` from daemon's error branch or as garbled frame-length bytes seen by the orchestrator/client side; (3) standalone one-shot mode works fine on the same input but multi-request daemon mode fails; (4) `IO.binread(:stdio, N)` returns `{:error, {:no_translation, :unicode, :latin1}}` despite the "bin" prefix suggesting it should be encoding-agnostic. Root cause is the escript default `:standard_io` encoding — it's `:unicode`, and `IO.binread` still routes through the io_server, which translates bytes to codepoints and errors when raw binary frames contain invalid UTF-8 sequences.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used when the user asks to "explore the app", "/qa", "auto-test the app", "find bugs in my app", "猴子测试", "自动探索", "随便点一下看看", "测一下全 app 看有没有崩", "smoke explore", or wants autonomous exploration of an Android or iOS app to surface crashes / ANRs / unexpected states. Drives the app via ui-mcp + mobile-mcp, tracks visited pages and clicked elements via report-mcp's state graph, captures crashes via log-mcp, and produces a Markdown report with bug list + coverage stats.

日本語の概要は準備中です。原文の説明を表示しています。

Diagnoses GKE workload failures (CrashLoopBackOff, OOMKilled, ImagePullBackOff, Pending, etc.) via logs and events. Use when pods fail to start or crash repeatedly. Don't use for GKE cluster infrastructure provisioning, node pool creation, or non-Kubernetes Google Cloud services.

日本語の概要は準備中です。原文の説明を表示しています。

git-finish crash window별 복구 기대치

日本語の概要は準備中です。原文の説明を表示しています。

システムログを分析してエラーの根本原因を特定し、段階的に深堀りしていくデバッグ専門家スキル。 アプリケーションログ、システムログ、クラウドサービスログなど様々な形式に対応。 5 Whys、タイムライン分析、Fishbone分析などのRCA（根本原因分析）手法を用いて 問題の本質を突き止め、再発防止策まで提案する。 Use when analyzing system logs to find error root causes, debugging application issues, or performing technical post-mortem analysis with log data. For organizational incident management processes (post-incident review, corrective action plans, incident reports without log data), use incident-rca-specialist instead. Triggers: "analyze this log", "find the root cause", "debug this error", "why is this failing", "log analysis", "what caused this crash", "troubleshoot this issue"

Recover a session after a hard crash (app died, machine rebooted, context lost mid-work): replay the turn-state ledger for the last requests, files and decisions, run a green/red health ping over the system map, file sync and MCP connectors, then rebuild the previous session history for pickup. Triggers: "/1", "resume after crash", "wake".

日本語の概要は準備中です。原文の説明を表示しています。

Diagnose and restore the Claude Desktop app when it will not start, crashes, or runs with no visible window. Use for Windows MSIX launch failures, 'Not main instance', stale AppX job containers, NeedsRemediation or 0x80070020 errors. Separates Desktop from the CLI, reads fresh logs, picks a narrow fix and proves a visible working window. Triggers: /claude-repair, Claude Desktop won't open, Claude app crashed.

日本語の概要は準備中です。原文の説明を表示しています。

Designs and reviews production observability for mobile apps on every stack — Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers crash and error reporting with usable context, breadcrumbs, structured logs, traces and custom metrics, release and cohort segmentation, correlation with backend requests, alerting thresholds, sampling and cost, and what must never be collected. Use when production problems cannot be explained from the available data, when setting up crash reporting or telemetry, or when defining release health alerts.

日本語の概要は準備中です。原文の説明を表示しています。

Configure and collect crash dumps for modern .NET applications. USE FOR: enabling automatic crash dumps for CoreCLR or NativeAOT, capturing dumps from running .NET processes, setting up dump collection in Docker or Kubernetes, using dotnet-dump collect or createdump. DO NOT USE FOR: analyzing or debugging dumps, post-mortem investigation with lldb/windbg/dotnet-dump analyze, profiling or tracing, or for .NET Framework processes.

日本語の概要は準備中です。原文の説明を表示しています。

Collect and analyze on-device performance metrics and crash diagnostics using MetricKit. Use when setting up MXMetricManager, handling MXMetricPayload or MXDiagnosticPayload, processing crash/hang/disk-write diagnostics via MXCallStackTree, adding custom signpost metrics, or uploading telemetry to an analytics backend.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a driver has been involved in a crash — regardless of preventability or injury — and the carrier needs to handle the immediate, post-investigation, and return-to-work phases. Covers trauma response, post-accident testing under 49 CFR 382.303, paid time off, retraining, light duty, and re-entry planning.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when a crash has been incorrectly classified as DOT-recordable when it shouldn't be. Covers the § 390.5 criteria, common misclassifications, and challenge process.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill to investigate a crash effectively. Covers preservation of evidence, witness statements, dashcam review, and reporting timeline.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill to document crashes that don't qualify as DOT-recordable. Covers what to track, why it matters, and insurance/CSA implications.

日本語の概要は準備中です。原文の説明を表示しています。

Systematic debugging methodology covering root cause analysis, log-based debugging, production debugging, memory leak diagnosis, deadlock detection, performance anomaly investigation, and reproducible bug reproduction. Distilled from real experience at Google, DeepMind, ByteDance, and Huawei. USE WHEN: investigating production incidents, debugging hard-to-reproduce bugs, analyzing crash dumps, finding race conditions, diagnosing memory leaks, debugging performance regressions, or any situation requiring systematic root cause analysis. Triggers on "debugging", "root cause", "bug", "crash", "segfault", "deadlock", "race condition", "memory leak".

日本語の概要は準備中です。原文の説明を表示しています。

既存機能の不具合を失敗するテストで再現し、修正、レビュー、承認後のコミットまで、各工程を担当するECCエージェントに割り振って進めるスキル。

• クラッシュや誤った出力を直したいとき
• 不具合の再発を検知するテスト作成
• 原因が分からない不具合の調査

DartとFlutterのコード作成・レビューで、状態管理、画面遷移、通信、例外処理、テストの実装パターンを参照し、アプリの構成を整えるスキル。

• 新機能の状態管理を選びたいとき
• null処理や非同期処理のレビュー
• ログイン状態で画面遷移したいとき

Runs a hypothesis-driven debugging loop across any language or binary, escalating to orthogonal oracle angles and locking the fix with a failing test. Use for crashes, silent failures, hangs, wrong responses, memory leaks, async misbehavior, or reverse engineering.

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

日本語の概要は準備中です。原文の説明を表示しています。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for reverse engineering, malware, DFIR, firmware, pwnable, and native exploit challenges. Use when the user asks to reverse a binary, unpack a sample, inspect a memory dump or PCAP, recover malware behavior, debug a crash, or build or verify an exploit chain under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

日本語の概要は準備中です。原文の説明を表示しています。

Deterministic orchestration graph runtime - declarative DAG pipelines with journal-based crash recovery

日本語の概要は準備中です。原文の説明を表示しています。

Replay-first debug flow for SGLang serving problems. Use when a live or recent server shows health-check failures, latency or throughput regressions, queue growth, timeouts, distributed stalls, crash dumps, wrong outputs after deploys, or PD/EP/HiCache issues, and the job is to turn the problem into a replay plus the right next debug tool.

日本語の概要は準備中です。原文の説明を表示しています。

End-to-end discipline for turning any large data source (audio libraries, email takeouts, document corpora, chat exports, API dumps) into brain pages at scale. The lifecycle spine: SCHEMA → ACCESS → TRIAL → EVALUATE → IMPROVE → CODIFY → TEST → SKILLIFY → BULK → MONITOR. State is tracked in a durable JSON manifest (see MANIFEST-PATTERN.md) so any crash, session boundary, or subagent fan-out resumes from ground truth instead of memory.

日本語の概要は準備中です。原文の説明を表示しています。

Diagnose GKE Cloud TPU training throughput drops and step-time regressions (15%+ TPU duty-cycle drop) using ML Diagnostics Workload Monitoring (`gcloud alpha mldiagnostics monitored-events` / `hypercomputecluster.googleapis.com/v1alpha`) and 1-minute Cloud Monitoring system metrics (`kubernetes.io/node/accelerator/*`). Distinguishes hardware and network fabric throttling from workload resource bottlenecks (HBM capacity, host memory, or host CPU saturation). Use when TPU training throughput or duty cycle drops without crashing pods, when `PERFORMANCE_DEGRADATION` monitored events fire, or when triaging slow multi-slice training steps. Don't use for complete multi-slice XLA execution stalls with `HANG_DETECTED` logs (use gke-ai-troubleshooting-tpu-mxla-hang) or pod eviction/interruption restarts (use gke-ai-troubleshooting-jobset-interruption).

日本語の概要は準備中です。原文の説明を表示しています。