「ai deployment」の検索結果

Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to self-hosted inference on GKE, or asks follow-up questions during such a migration (hardware sizing, model staging, manifest generation, validation, traffic cutover). DO NOT use for brand new GKE inference deployments with no existing workload to migrate (use gke-inference instead). DO NOT use if the user intends to automate the migration via the Gemini Cloud Assist MCP server.

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。

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

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。

Design, configure, debug, and optimize OpenClaw AI agent deployments. Master guide for gateway configuration, openclaw.json settings, model routing and fallback chains, skills development and publishing, cron job scheduling, memory systems (Qdrant, Neo4j, SQLite), Docker infrastructure, and Tailscale VPN networking. Includes config analyzer that audits your openclaw.json and suggests improvements, plus health checker that validates all OpenClaw subsystems. Built for AI agents — Python stdlib only, no dependencies. Use for OpenClaw setup, gateway debugging, skill building, cron management, model optimization, cost reduction, and infrastructure troubleshooting.

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。

Plan and implement infrastructure, containers, CI/CD, deployment safety, observability, and rollback changes. Use for Docker, IaC, cloud runtime, Kubernetes, pipeline, deployment, or operational reliability work. Does not read dotenv files or approve/release deployments.

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。

Design deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits. Best for teacher-student compression, threshold design, and failure-aware deployment. Activate on "model compression", "teacher- student", "distillation score", "cascade model", "edge deployment", or "model calibration". NOT for generic deep-learning overviews, prompt optimization, or training work without a concrete distillation objective.

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。

Cloud guardrails for any vendor workload — Google Cloud (GCP, Vertex AI, GKE), AWS (IAM, EKS, Bedrock), Azure (Entra ID, Policy, AKS), Alibaba Cloud (RAM, mainland/international residency). Enforces identity least-privilege, mechanical policy, data boundaries, residency, cost caps, network egress and observability, with official-source validation before any claim. Trigger on any cloud infrastructure design, review, Terraform plan, or LLM/agent deployment; the-architect routes 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。

Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This i...

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。

Build AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.

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。

Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS"...

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 working with Aws Appconfig — aWS AppConfig configuration management and deployment analysis. Covers applications, environments, configuration profiles, deployment strategies, hosted configurations, and deployment status monitoring. Use when inspecting AppConfig deployments, reviewing feature flags, auditing configuration profiles, or debugging deployment rollbacks.

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。

Design and create Busabase-backed App-in-Skill packages with a canonical app project, Busabase-native setup and product onboarding, review/execution workflows, a standardized responsive UI, and AirApp-first delivery. Use when a user wants a Busabase research desk, review queue, planner, action console, operating dashboard, control panel, collaboration workspace, onboarding/readiness flow, or an existing Kelly App-based skill updated. Every generated skill contains a complete app/ project, deploys that source to Busabase AirApp by default, runs pnpm dev only when local preview is explicitly requested, follows the Kelly desktop and phone UI contract, delegates AirApp runtime, SDK, security, scaffolding, and deployment constraints to busabase-app-creator, and delegates repository-level conformance testing to kelly-app-skill-creator-tests.

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。

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment 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。

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

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。

Design AI model deployment strategies with rollout. TRIGGERS - Use when user needs help with ai-deployment-strategy related tasks.

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。

Design canary deployment strategies for AI. TRIGGERS - Use when user needs help with ai-canary-deployment related tasks.

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。

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

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 and troubleshoot WorkOS AuthKit authentication issues with Convex. Use when authentication fails, JWT validation errors occur, user identity returns null, email claims are missing, admin access checks fail, or sign in button does not work. Supports Netlify deployment.

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 deployment engineer specializing in modern CI/CD pipelines, GitOps workflows, and advanced deployment automation. Masters GitHub Actions, ArgoCD/Flux, progressive delivery, container security, and platform engineering. Handles zero-downtime deployments, security scanning, and developer experience optimization. Use PROACTIVELY for CI/CD design, GitOps implementation, or deployment automation.

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 containerizing applications with Docker, creating Dockerfiles, docker-compose configurations, or deploying containers to various platforms. Ideal for Next.js, React, Node.js applications requiring containerization for development, production, or CI/CD pipelines. Use this skill when users need Docker configurations, multi-stage builds, container orchestration, or deployment to Kubernetes, ECS, Cloud Run, etc.

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。

Safety-case design for always-on agents with episodic memory. Covers data hygiene, privacy and security risk surfaces, cost controls, scope, user dependency concerns, retention, incident containment, and responsible deployment evidence. It identifies hazards and asks for named controls, tests, owners, and residual uncertainty; it does not provide legal, clinical, youth-safety, or jurisdictional determinations. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).

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。

AIエージェント向けスキルの新規作成・改訂・公開前検証を、テストから進めるスキル。導入前後の振る舞いを比較し、指示の抜けや誤解を修正します。

• 再利用できるスキルを作りたいとき
• 誤解される指示を改訂したいとき
• 公開前にスキルを検証したいとき

公開前の文章やコードを二つの独立したエージェントが同じ基準で審査し、両方の合格まで修正と再審査を繰り返し、上限を超えたら人へ判断を引き継ぐスキル。

• 公開前に文章の事実関係を確認したいとき
• 顧客向け文章とブランドルールの照合
• 本番配布前に独立した審査を加えたいとき

Quarkusサービスの変更後や公開前に、ビルド、コード解析、テスト、脆弱性検査を順に確認するスキル。ネイティブ実行形式やコンテナ、環境設定の検証も扱います。

• QuarkusのPR前チェック
• リファクタリングや依存更新の検証
• ネイティブ実行形式の互換性確認

AIエージェントの危険なコマンドを実行前に確認し、ファイル編集を指定ディレクトリ内に制限します。単独または併用のモードで、本番作業や自律実行時の操作を管理します。

• 本番作業で危険なコマンドを確認
• 特定ディレクトリだけ編集したいとき
• 自律実行時に保護を併用したいとき