「step」の検索結果

Diagnose GKE Cloud TPU multi-slice training hangs (Megascale `HANG_DETECTED` logs and hang monitored events) using the ML Diagnostics `Megascale XLA (MXLA) Hang Analyzer` (`gcloud alpha mldiagnostics monitored-events`) and 1-minute Cloud Monitoring multi-slice latency metrics (`kubernetes.io/container/multislice/*`). Distinguishes XLA compiler/HLO launch divergence and host data-input stalls from TPU chip, SparseCore, ICI, or network fabric faults. Use when multi-slice TPU training jobs freeze without progressing steps, emit `HANG_DETECTED`, or stall in collective operations. Don't use for gradual step-time throughput drops without hangs (use gke-ai-troubleshooting-tpu-performance-degradation) or pod preemption/eviction 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。

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。

Create/edit parametric CAD models, organize CAD projects, export STEP/STL/3MF/GLB files, resolve prompt references, and measure geometry with cadgen. Open and visually review existing STEP/STP, STL, 3MF and GLB files in CAD Viewer.

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。

Take a guided tour of Open SaaS — walks you through step-by-step through the project structure, features, and customization checklist.

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。

Walk the user through design or implementation decisions one at a time, or review completed code one change at a time. Use when the user says "walk me through each decision", "let's decide together", "help me work through these choices", "walk me through what you did", or "QA step by step". Present concise context, mutually exclusive options, log each answer, and finish with a summary.

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。

Reads and changes a running CARLA server's WorldSettings via apply_settings — synchronous vs asynchronous mode, fixed_delta_seconds (fps), physics substepping, and no_rendering_mode — and keeps the Traffic Manager's sync state matched automatically. Use when the user asks to "enable/disable sync mode", "set synchronous mode", "run at a fixed fps/step", "turn off rendering", "make the sim deterministic", or "restore async mode", and as the prerequisite for keeping settings across a map load.

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。

Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

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。

Resilient multi-step workflow for spreadsheet processing when execute_code_sandbox fails, using shell_agent exploration, file-based scripts, and verification steps

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。

Search reusable OpenSpace skills locally, and browse cloud packages step by step when a cloud skill may help. Reusing proven skills saves tokens, improves reliability, and extends your capabilities beyond built-in tools.

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。

Step-by-step audio production with per-stem verification, timing alignment, and incremental quality gates

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。

Creates step-by-step decomposition plans and migration roadmaps for breaking apart monolithic applications. Use when asking "what order should I extract services?", "plan my migration", "create a decomposition roadmap", "prioritize what to split", "monolith to microservices strategy", or tracking decomposition progress. Do NOT use for domain analysis (use domain-analysis) or component sizing (use component-identification-sizing).

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 planning legacy system migrations, codebase modernization, monolith decomposition, microservices consolidation, cross-language rewrites, or framework upgrades. Invoke for strangler fig pattern, incremental migration strategy, or refactoring roadmaps. Do NOT use for domain analysis (use domain-analysis), component sizing (use component-identification-sizing), or step-by-step decomposition plans (use decomposition-planning-roadmap).

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。

Step-by-step explanation of a concept with progressive depth

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。

Start a new OpenSpec change using the experimental artifact workflow. Use when the user wants to create a new feature, fix, or modification with a structured step-by-step approach.

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。

Creates durable, resumable workflows using Vercel's Workflow DevKit. Use when building workflows that need to survive restarts, pause for external events, retry on failure, or coordinate multi-step operations over time. Triggers on mentions of "workflow", "durable functions", "resumable", "workflow devkit", or step-based orchestration.

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 by default for non-trivial multi-step work that needs persistent planning, progress tracking, or durable notes on disk. Trigger when a task will likely span multiple tool calls, research steps, verification loops, or enough context that the plan should not live only in transient chat 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。

Write clear GitHub Actions step summaries with progressive disclosure.

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。

Guide for building high-quality MCP (Model Context Protocol) servers and tools: what MCP is, how to design tools/resources/prompts for agent usability, and how to implement servers in TypeScript or Python. Use when you need MCP best practices, server architecture patterns, or a step-by-step build workflow (with language preference).

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。

Hunt MFA / 2FA bypass — 7 distinct patterns. (1) MFA not enforced on sensitive endpoints (password change, email change accept without MFA challenge), (2) MFA-step skip via direct navigation to post-login URL, (3) MFA-token replay (same code accepted twice), (4) brute-force the 6-digit OTP without rate limit (10^6 attempts at server speed), (5) race condition on OTP validation, (6) recovery-code dump via /api/me, (7) backup factor downgrade (SMS factor with no rate limit). Plus the chain: cookie theft + password oracle + no step-up = ATO without MFA challenge. Detection: trace auth flow in Burp, find every state transition, check if MFA is middleware-gated vs per-endpoint, check OTP entropy and rate limit on OTP-validate. Validate: attacker session reaching post-MFA state. Use when hunting auth bypass, MFA flows, chaining primitives toward ATO.

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。

Step-by-step migration guides for moving to Playwright from Cypress or Selenium/WebDriver — command mappings, architecture changes, and incremental adoption strategies.

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。

Activate this agent for any future-oriented question that requires deep quantitative analysis, historical precedents, and structured scenario planning. Triggers include: "Will [X]?", "Who will win [X]?", "What happens to [X]?", prediction requests with high stakes, foresight analysis, STEEEP scenario planning, futures cone, competitive race analysis, technology adoption curves, geopolitical shifts, or any question about a future outcome that deserves rigorous multi-step analysis. This agent runs a 12-step deterministic pipeline: Claude handles intelligence, Python handles all arithmetic. Year is NOT required — the engine infers the horizon. REQUIRES: Bash tool + Python 3.x. Not compatible with claude.ai web (use Soft Predict Future instead).

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。

Create Earth2Studio diagnostic model wrappers for single-step data transformations, including simple derived diagnostics, packaged AutoModel diagnostics, and generative or diffusion diagnostics. Do NOT use for prognostic time-stepping models, data sources, or installation.

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。

Start a new OpenSpec change using the experimental artifact workflow. Use when the user wants to create a new feature, fix, or modification with a structured step-by-step approach.

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。

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

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。