「failures」の検索結果

Playwrightでブラウザ操作を再現するテストを作り、画面ごとの操作整理や自動実行を支援します。時々失敗するテストの調査と、画像・動画・実行記録の保存も扱います。

• ログインや検索の動作確認
• 画面ごとのテスト操作を整理したいとき
• 時々失敗するテストを調べたいとき

TypeScript、Python、Goのエラー処理を設計・見直すスキル。エラーの分類、再試行、APIの応答、利用者向けの案内文を整理します。

• 新しいサービスのエラー分類の設計
• 外部サービスの一時障害を再試行したいとき
• APIのエラー応答とログの見直し

定期ジョブ、フック、MCPサーバー、外部連携などの設定と稼働状況を証拠付きで棚卸しし、故障や重複、不足を整理して、維持・統合・廃止・修正の優先順位を示すスキル。

• 実際に動いている自動化を確認したいとき
• 故障したジョブや認証切れを探したいとき
• フック・スクリプト・外部連携の重複整理

REST・GraphQL APIの不具合を、接続、認証、送受信データの順に切り分けます。再現用コマンドや回帰テストを整え、原因と修正案をまとめるスキル。

• APIの認証エラーを調べたいとき
• Postmanとコードの動作差を再現
• ページ分割や回数制限の確認

Diagnose and fix browser, preview, or Electron export/download failures, especially image export issues involving Save As, Blob/Data URLs, the File System Access API, createWritable failures, and 0 KB files.

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 persistent-storage failures — volume attach/mount errors (Regional PD on optimized VMs, fsGroup mount timeouts), disk-performance and node storage-pressure issues, slow-disk Pod-creation failures, volume-expansion problems, Local SSD / Hyperdisk Storage Pool creation errors, and Cloud Storage FUSE OOM. Use when Pods are stuck in ContainerCreating, volumes fail to attach or mount, or nodes report storage pressure. Don't use for routine storage provisioning or StorageClass/PVC authoring (see the gke-storage skill).

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。

Put your AI on a Performance Improvement Plan. Forces exhaustive problem-solving with Western big-tech performance culture rhetoric and structured debugging. Trigger when: (1) task failed 2+ times or stuck tweaking same approach; (2) about to say 'I cannot', suggest manual work, or blame environment without verifying; (3) being passive—not searching, not reading source, just waiting; (4) user frustration: 'try harder', 'stop giving up', 'figure it out', 'again???', or similar. Also for complex debugging, env issues, config/deployment failures. All task types: code, config, research, writing, deployment, infra, API. Do NOT trigger on first-attempt failures or when a known fix is executing.

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。

Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference performance and memory; debugging multi-node/ multi-GPU hangs; and writing/running tests. Use when the user is training or fine-tuning large models, hits low TFLOPS/MFU, OOM, slow dataloading, a loss spike/divergence, a NCCL/InfiniBand or multi-node hang, node/GPU failures, checkpoint or preemption problems, storage/network bottlenecks, or needs to pick GPUs/cloud/file-systems or size inference latency/throughput. Distilled from "Machine Learning Engineering", the latest version of which can be found at https://github.com/stas00/ml-engineering The latest SKILL.md version can be found at https://github.com/stas00/ml-engineering/blob/master/skills/ml-engineering/SKILL.md

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。

Classify CI failures — distinguish clear regressions from infra flakes and security-test failures. Produces structured failure reports.

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。

Identify and handle pre-execution agent failures occurring before any iterations or tool usage

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。

Analyze MSBuild binary logs to diagnose build failures. USE FOR: build errors that are unclear from console output, diagnosing cascading failures across multi-project builds, tracing MSBuild target execution order, and generally any MSBuild build issues. Requires an existing .binlog file. DO NOT USE FOR: generating binlogs (use binlog-generation), non-MSBuild build systems.

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。

[omh] Hidden or swallowed errors suspected: find swallowed errors, unsafe fallbacks, hidden UI/runtime failures, and missing propagation before they become false green status. Use when the user says: failure-signal-audit, failure signal audit, silent failure, silent failures, silent failure hunter, swallowed error, swallowed errors, empty catch.

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 your agent or environment is broken — wrong answers, errors, timeouts, tool failures, or CLI issues. Reads traces and logs to diagnose root causes. Also checks prerequisites when the CLI itself isn't working. Triggers on: "agent not working", "wrong answer", "agent error", "tool call failing", "debug agent", "check logs", "read traces", "broken", "500 error", "424 error", "model access denied", "command not found", "stuck in DELETING", "maxVms exceeded", "cold start diagnosis", "cold start slow", "agentcore create error", "create failed", "exit code 7", "connection refused local dev". Not for deploy failures — use agents-deploy. Not for performance tuning without errors — use agents-optimize. Not for VPC configuration — use agents-build. Not for observability setup or missing logs — use agents-optimize.

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。

Troubleshoots failing applications by discovering and analyzing CloudWatch log groups to identify error patterns, root causes, and actionable solutions. Use when an application is experiencing failures and log-based diagnosis is needed.

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 and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments. Provisioned uses aws mwaa invoke-rest-api, CloudWatch log groups, and get-environment; Serverless uses aws mwaa-serverless API (GetWorkflowRun, ListWorkflowRuns, GetTaskInstance) and CloudWatch logs. Covers failed runs and tasks, DAGs not appearing, import errors, worker OOM, IAM denials, and dependency drift. Triggers on: DAG failed, task failed, workflow run failed, MWAA error, debug my DAG, why did my workflow fail, DAG not showing up, MWAA import error, requirements failing, worker crashed, serverless run failed. Not applicable to authoring workflows (handled by authoring-mwaa-workflow), running or smoke-testing a workflow (handled by testing-mwaa-workflow), or CI-CD deploy failures.

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 for AI-assisted Unity work that needs live repo discovery, project-derived routing, runtime-owner proof, runtime-visible output hard stops, runtime numeric proof for repeated visible-output failures, state-step guards, multi-agent scope ownership, modular C#/asmdef safety, UI/scene/visual asset gates, data-first content changes, validation, cleanup proof, or durable workflow rules. Best when agents must prove the actual folder/module/scene/prefab/runtime owner before editing, especially runtime UI, generated assets, code graphs, tutorial/state flows, guided selection/action flows, overlay/dim source-bound mistakes, coordinate conversions, focus/highlight/marker/HUD alignment, predictive lead aiming, ballistic trajectories, spatial IoU verification, or repeated "fix still not visible" failures.

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 SLCI code inspection has failed due to compatibility issues and you need to diagnose the incompatibilities. Traces inspection failures back to root-cause incompatibility settings or known limitations by matching failed code lines against known incorrect code generation patterns and documented limitation scenarios. Hands off to slci-compatibility-analysis for fixes. Trigger keywords: inspect, inspection failed, trace inspection errors, why did inspection fail, SLCI verification failures, limitation.

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。

Debug systematically with root-cause analysis before fixes. Use for bugs, test failures, unexpected behavior, performance issues, CI failures, or system investigation.

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。

Periodically analyze open pull requests for CI failures, code review feedback, and quality issues, then fix them in batch. Use this skill when the user asks to check PR status, triage CI failures, fix review comments, analyze open PRs, or run a scheduled PR health check across the repository.

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 real run evidence, validation failures, source drift, platform drift, and user feedback to plan and, only after explicit approval, apply structural upgrades to an existing skill. Use when the user asks to improve an existing skill from recent runs, recurring failures, outdated sources, excessive bloat, changed platform behavior, or validated workflow feedback. Do not use to create a brand-new skill or to execute the business workflow itself.

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。

Run tests and systematically fix all failing tests using smart error grouping. Use when user asks to fix failing tests, mentions test failures, runs test suite and failures occur, or requests to make tests pass.

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。

Analyzes recent CI failures on pull requests to identify flaky tests, using retry outcomes (failed attempt → green re-run) and cross-PR recurrence as evidence, and maintains a local longitudinal ledger so flakiness can be tracked over time. TRIGGER when: the user wants to find flaky tests, correlate recent CI failures, check which tests fail across PRs or recover on retry, or refresh the flakiness trend report. DO NOT TRIGGER when: babysitting a single PR's CI until green → monitoring-pull-requests; diagnosing or fixing one specific failing test → the bug-analysis skills.

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。

Self-healing codebase system. Monitors runtime errors, test failures, and build breaks. Automatically diagnoses root cause, generates fix, validates fix, and applies if safe. Zero human intervention for routine failures.

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。

Analyze MSBuild binary logs to diagnose build failures. USE FOR: build errors that are unclear from console output, diagnosing cascading failures across multi-project builds, tracing MSBuild target execution order, and generally any MSBuild build issues. Requires an existing .binlog file. DO NOT USE FOR: generating binlogs (use binlog-generation), non-MSBuild build systems.

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。