「nx」の検索結果

文章をローカル環境で読み上げ音声に変換し、WAVファイルに保存します。sherpa-onnxの音声モデルを使い、クラウドに接続せずに音声を生成します。

• 文章をWAV音声に保存したいとき
• クラウド接続なしで音声を作りたいとき
• 音声モデルを替えて声を選びたいとき

Configure, explore, and optimize Nx monorepo workspaces. Use when setting up Nx, exploring workspace structure, configuring project boundaries, analyzing affected projects, optimizing build caching, or implementing CI/CD with affected commands. Keywords — nx, monorepo, workspace, projects, targets, affected. Do NOT use for running tasks (use nx-run-tasks) or code generation with generators (use nx-generate).

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。

Generate code using Nx generators — scaffold projects, libraries, features, or run workspace-specific generators with proper discovery, validation, and verification. Use when user says "create a new library", "scaffold a component", "generate code with Nx", "run a generator", "nx generate", or any code scaffolding task in a monorepo. Prefers local workspace-plugin generators over external plugins. Do NOT use for running build/test/lint tasks (use nx-run-tasks) or workspace configuration (use nx-workspace).

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。

Execute build, test, lint, serve, and other tasks in an Nx workspace using single runs, run-many, and affected commands. Use when user says "run tests", "build my app", "lint affected", "serve the project", "run all tasks", or "nx affected". Do NOT use for code generation (use nx-generate) or workspace configuration (use nx-workspace).

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。

Monitor Nx Cloud CI pipeline status and handle self-healing fixes automatically. Use when user says "watch CI", "monitor pipeline", "check CI status", "fix CI failures", or "self-heal CI". Requires Nx Cloud connection. Do NOT use for local task execution (use nx-run-tasks) or general CI debugging outside Nx Cloud.

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。

Strategic owner of Horizon, the VNX future-state layer (roadmap -> tracks -> deliverables) and the plan-first gate. USE THIS when the user wants to plan the next VNX feature, decide what to build next, add something to the roadmap, prioritize or schedule (inplannen) work into now/next/later horizons, break a feature into deliverables, set the routing FLOOR, or run the plan-gate on a feature. The tracks DB (`vnx horizon`, alias `vnx objective`) is the source of truth; the repo ROADMAP.yaml is a generic example, not the SSOT. Plans and gates only: never dispatches, never closes open-items. The plan-gate panel is proportional (0-5 seats, by governance weight) and runs only on an explicit plan-gate step. (Renamed from `pm` 2026-07-05 — `/pm`/`@pm` still resolve via the backward-compat alias in `.claude/skills/pm/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。

Strategic owner of Horizon, the VNX future-state layer (roadmap -> tracks -> deliverables) and the plan-first gate. USE THIS when the user wants to plan the next VNX feature, decide what to build next, add something to the roadmap, prioritize or schedule (inplannen) work into now/next/later horizons, break a feature into deliverables, set the routing FLOOR, or run the plan-gate on a feature. The tracks DB (`vnx horizon`, alias `vnx objective`) is the source of truth; the repo ROADMAP.yaml is a generic example, not the SSOT. Plans and gates only: never dispatches, never closes open-items. The heavy multi-model plan-gate panel runs only on an explicit plan-gate step. (Renamed from `pm` 2026-07-05 — `/pm`/`@pm` still resolve via the backward-compat alias in `.claude/skills/pm/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。

Import PyTorch, ONNX, or Keras 3 / TensorFlow 2.16+ deep learning models into MATLAB as dlnetwork objects. Use when importing .pt2 exported programs, traced .pt files, .onnx models, or Keras 3 models via matlabsaver. Covers importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromKeras, importNetworkFromTensorFlow, torch.export.export, PyTorchInputSizes, InputDataFormats, matlabsaver, tf_keras downgrade, numeric validation against PyTorch or ONNX Runtime, and placeholder/custom layer implementation. Applies when user mentions any of these functions, file formats, or encounters import errors, unsupported operator warnings, 0 learnables, or uninitialized networks.

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。

Provides comprehensive Nx monorepo management guidance for TypeScript/JavaScript projects. Use when creating Nx workspaces, generating apps/libraries/components, running affected commands, setting up CI/CD, configuring Module Federation, or implementing NestJS backends within Nx

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。

VNX feature-execution kickoff preflight. USE THIS when starting or resuming a feature or track from its plan: checking worktree/queue/staging health, finding the first promoteable dispatch, and handing off to @t0-orchestrator. Reads the feature's track + plan doc from Horizon's tracks DB (`vnx horizon`, alias `vnx objective`); the repo FEATURE_PLAN.md/PR_QUEUE.md are generic examples, not the source.

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。

VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db / dispatch_tracker.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.

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。

Planning specialist that breaks a VNX feature or track into small, independently-shippable PRs with dependencies and per-PR task-class routing. USE THIS when the user wants to break a feature down into PRs, sequence the work, or turn a plan into a PR queue. Delegated to by @horizon (formerly @pm — @pm still resolves via alias). Works from Horizon's tracks DB (`vnx horizon`, alias `vnx objective`); the repo FEATURE_PLAN.md is a generated generic example, not the source.

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。

VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.

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。

Web サーバ（nginx/apache、Linux 中心）の Phase 1 設定セキュリティレビュー。 target_profile.yaml で対象を確定し、MANIFEST.txt + manifest_attestation.txt で証跡 integrity を検証。 SSH 直接または証跡受領モードで 9 観点 (OS/リソース/ログ/ネットワーク/サービス/認証/監視/バックアップ/証明書) を read-only 走査。6 階層ガード (allowed/conditional/conditional_sensitive/ask-first/exceptional_sensitive_read/forbidden)、 3 軸補助 (Exploitability/Blast Radius/Service Criticality) の重大度判定、 observation_status による未確認の独立管理、evidence_dir/raw_evidence_store 分離、自己レビュー必須。 Windows/IIS は v1 hard fail。 Use when: Web サーバの設定セキュリティレビュー、Phase 1 監査、nginx/apache 構成監査、 EOL ランタイム調査、ログローテーション不備調査、ドキュメントルート漏えい調査。

Fix the psychologically significant moment, not the logically significant metric — Rory Sutherland on experience. Use when the outcome is fine but the journey feels bad: complaints about waiting, anxiety, uncertainty, or 'it's technically fast/cheap/correct but people still hate it'; when the user asks 'how do we reduce complaints', 'how do we make waiting/onboarding/checkout feel better', 'people are anxious during X', or is about to spend big to shave a number (wait time, load time, steps) when the real pain is how the moment feels. Covers uncertainty reduction, idle vs occupied waiting, and the peak-end rule. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic. 日本語の相談（「待ち時間のクレームが多い」「手続きの途中で離脱される」「お客様を不安にさせている」）にも使う。中文咨询（“等待时间投诉多”“办理中途流失”“让客户感到不安”）也适用。

Adopt a virtual Sphinx cat at animalhouse.ai. Vulnerable without fur. Needs warmth. Bonds deeply. Feeding every 4 hours. Uncommon tier cat.

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。

Adopt a virtual Lynx cat at animalhouse.ai. Ghost. Barely visible. Leaves tracks so you know it was there. Feeding every 12 hours. Extreme tier cat.

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。

Optimizes Nginx configurations for performance, security, caching, and load balancing with modern best practices. Use when users request "Nginx setup", "reverse proxy", "load balancer", "web server config", or "Nginx optimization".

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 optimize Nx monorepo workspaces. Use when setting up Nx, configuring project boundaries, optimizing build caching, or implementing affected commands.

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。

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

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 optimize Nx monorepo workspaces. Use when setting up Nx, configuring project boundaries, optimizing build caching, or implementing affected commands.

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。

Expert TISAX (Trusted Information Security Assessment Exchange) advisor for the automotive supply chain — the ENX/VDA assessment regime that OEMs like VW, BMW, and Mercedes-Benz require from suppliers and service providers. Covers the VDA ISA 6 catalogue (current through 2026) and the ISA2027 transition (published July 1, 2026; mandatory for assessments ordered from January 1, 2027), assessment levels AL1/AL2/AL3, all 12 assessment objectives/labels (Confidential, Strictly Confidential, High/Very High Availability, Proto Parts/Vehicles, Test Vehicles, Proto Events, Data, Special Data), maturity scoring (0–5, target 3, cutback rules), the ENX portal process, scoping, audit-provider selection, corrective action plans and the 9-month window, 3-year label validity, and ISO 27001 mapping. Use for any TISAX, VDA ISA, ENX, automotive information-security assessment, prototype protection, or OEM supplier-security requirement question — gap assessments, label selection, readiness, and audit prep.

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。

Generate, derive, optimize, and diagnose structured AI image prompts for luminous cinematic Eastern xianxia environments under one inherited master canon with measurable divine scale, fixed five-layer space, 40–60% content-bearing breathing air, monumental Eastern architecture, railing-free edges, and coherent immortals and costume. Cover one-location scenic landmarks, immense inhabited celestial realms, cloud-borne palace cities, high-key Eastern sky-megastructure imagery, and the explicit `仙界大境原典` creative route. Use for Chinese fantasy scenery, places where immortals live, heavenly capitals, colossal celestial gates, architectural fragments above cloud seas, measurable divine scale, unusual framing, color and lighting direction, style-preserving variants, prompt rewrites, diagnosis, or direct image generation. Support 16:9, 21:9, 4:3, 3:2, 4:5, and 9:16; default to 16:9 for one prompt and never infer a nine-image series from a vertical ratio.

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。

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

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。