「ai deployment」の検索結果

Deploy containerized applications to AWS EC2 and expose them publicly via Cloudflare Tunnel with automatic HTTPS. Eliminates need for load balancers, SSL certificates, or public inbound ports.

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Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications.

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。

Coinbase AgentKit - Toolkit for enabling AI agents with crypto wallets and onchain capabilities. Use for building autonomous agents that can execute transfers, swaps, DeFi operations, NFT minting, smart contract deployment, and gasless transactions via Smart Wallets.

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。

Maintain the standalone cmd/authdb server, pkg/httpserver listener and portal routing, HTTP configuration parser, TLS, shutdown, executable tests, and deployment guidance. Use authdbctl for the management client.

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Use before any deployment or completion claim. Enforces test gates, evidence-based verification, and frontend safety checks. No deploy without passing. No claims without evidence.

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。

AFFiNE is an open source workspace that combines docs, whiteboards, and lightweight databases in one product. This skill is useful when an agent needs to structure notes, maintain linked knowledge, or automate content workflows around AFFiNE workspaces and self-hosted deployments.

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The Mileva Method (CRISP) — Phase 5: Prove. Success validation against Phase R baseline metrics, test log review, and AI justification validation. Use after deployment. Triggers on "prove", "phase 5", "validate", "did it work", "success criteria", "measure results", or after go-live.

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DevOps patterns: containerization, CI/CD, deployment strategies, monitoring. Use when containerizing apps, setting up pipelines, or deploying services.

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Design and execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout.

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Apply ant colony optimization and foraging theory to resource search, exploration-exploitation tradeoffs, and distributed discovery. Covers scout deployment, trail reinforcement, diminishing returns detection, and adaptive foraging strategy selection. Use when searching a large solution space where brute-force enumeration is impractical, balancing investment between exploring new approaches and deepening known good ones, optimizing resource allocation across uncertain opportunities, or diagnosing premature convergence on local optima.

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Create general-purpose Dockerfiles for Node.js, Python, Go, Rust, and Java projects. Covers base image selection, dependency installation, user permissions, COPY patterns, ENTRYPOINT vs CMD, and .dockerignore. Use when containerizing an application for the first time, creating a consistent build/runtime environment, preparing an app for cloud deployment or Docker Compose, or when no existing Dockerfile is present in the project.

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Use when the user asks to rank deployment platforms and stacks against their product with quantitative trade-offs. Not for source or remote mutation.

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 human invokes this skill to build a webhook UI that wakes a bot. Not for unpreviewed or unapproved credential, host, Tailscale, deployment, remote, paid, or irreversible changes.

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Interactively gather parameters for `python -m cli.inference.throughput_optimizer`, generate a deployment simulation command, explain assumptions, ask for execution confirmation, then run the simulation and summarize the best parallel strategy. Use when the user wants to evaluate a model on one or more hardware profiles, compare hardware targets, compare aggregation versus disaggregation versus PD ratio optimization, choose TP/EP/MOE-DP search ranges, or obtain recommended concurrency, throughput, TTFT, and TPOT results from throughput modeling.

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Reviews deployment plans for completeness, rollback procedures, and monitoring setup. Use when reviewing deployment plans before approval in Operations Phase.

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。

[Alpha] Experimental Azure environment builder for infrastructure and deployment design. Use when building Azure infrastructure, generating Bicep with AVM modules, deploying apps to App Service/AKS/Container Apps, designing Hub-Spoke/AKS/AI Foundry architectures, or creating low-cost Azure operations demos.

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。

Serverless GPU compute platform for AI model deployment — web endpoints, GPU functions, model serving, and TypeScript client patterns

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Replicate SDK patterns for TypeScript/Node.js -- client setup, predictions, streaming, webhooks, file handling, model versioning, deployments, and training

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LiteLLM proxy server setup, TypeScript client patterns via OpenAI SDK, model routing, fallbacks, load balancing, spend tracking, virtual keys, and production deployment

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Prepare logical commits, publish through Pharos’s protected-main pull-request gate, monitor deployment, and hand off operational acceptance. Use only when commit, push, publish, release, or deployment watching is requested.

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。

.NET guidance — ASP.NET Core APIs, Entity Framework Core, DI, configuration, testing, and container deployment.

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Use when generating or editing diagrams, charts, visual assets, posters, UI mockups, product images, infographics, academic figures, comics, avatars, storyboards, brand boards, or image-edit workflows, especially when exported PNGs need visual QA and bounded self-repair. Prefer deterministic Mermaid/SVG/HTML/canvas sources for text-heavy diagrams; use HTML technical diagrams for browser-ready system blueprints, architecture, deployment topology, agent runtime, memory flow, before-after architecture, workflow, sequence, data-flow, lifecycle, runbook, PII/data-lineage, and state-machine diagrams. Do not use for ordinary UI polish without generated imagery.

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Creates Pulumi infrastructure-as-code projects in Python, defines cloud resources (AWS, Azure, GCP), configures ESC environments for secrets management, sets up OIDC authentication for secure deployments, and builds multi-language component resources. Use when creating Pulumi Python projects, writing infrastructure code, configuring cloud providers, managing secrets with Pulumi ESC, setting up OIDC for Pulumi, automating infrastructure deployments with Python, creating reusable Pulumi components in Python, or configuring Python toolchains (pip, poetry, uv) for Pulumi. Also use when the user mentions pyproject.toml with Pulumi, component_provider_host, or Python virtual environments for infrastructure code.

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Use for hands-on Pulumi CLI work: running deployments, fixing broken stacks, and managing infrastructure state. Handles: recovering from stuck or interrupted `pulumi up` with pending operations, cleaning orphaned resources from state after out-of-band cloud deletions, protecting critical resources from accidental `pulumi destroy`, moving resources between stacks without recreating them, targeting specific resources during deployment, migrating between backends (local file to Pulumi Cloud, S3), stack lifecycle management, state export/import/repair, CI/CD pipeline setup, and importing existing cloud resources. Use this skill — not the language-specific Pulumi skills — whenever the user's question is about operating, troubleshooting, or recovering Pulumi infrastructure rather than writing program code.

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。