「deployment」の検索結果

Use when implementing, integrating, upgrading, debugging, or answering anything involving third-party APIs, libraries, frameworks, CLIs, cloud services, model/provider SDKs, fast-moving product behavior, user requests for latest/current/official behavior, unfamiliar repo docs/specs, errors that may indicate API drift, or high-stakes auth, security, billing, data, migration, deployment, compliance, or privacy behavior. Forces Codex to web-search for current official docs and read primary docs before assuming from 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。

Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform

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。

Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management

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。

Cut and maintain Observal release branches, prepare alpha, beta, RC, stable, and patch releases, backport merged main PRs, inspect release status, verify published artifacts, and recover failed release operations. Use for Observal maintainer release tasks and release-process changes, not for installing or upgrading an existing Observal 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。

Deploying Expo apps to iOS App Store, Android Play Store, web hosting, and API routes

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。

Helps understand and write EAS workflow YAML files for Expo projects. Use this skill when the user asks about CI/CD or workflows in an Expo or EAS context, mentions .eas/workflows/, or wants help with EAS build pipelines 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。

Use when creating new skills, editing existing skills, or verifying skills work before 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。

Build, validate, publish, update, inspect, query, roll back, or archive a dynamic Marina applet. Use for Artifact+-style web apps that should run behind Marina authentication, carry small bundled assets or data, use an applet-owned Postgres schema, or add a trusted Python ASGI backend without a Marina image 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。

World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production 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。

World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.

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 working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and 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。

Full-stack software development agent for design, implementation, testing, and deployment. Use when the user explicitly asks for end-to-end project creation, feature development, bug fixing, or code refactoring.

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。

Deploy status across all projects. Shows ECS service versions, Vercel deployments, recent deploys, pending deploys, and CI/CD pipeline state.

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。

Automate Vercel tasks via Rube MCP (Composio): manage deployments, domains, DNS, env vars, projects, and teams. Always search tools first for current schemas.

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。

Turn any codebase into evidence-grounded Markdown docs plus a machine-readable index.json. Every claim cites its source; never invents deployment 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。

Automate Render tasks via Rube MCP (Composio): services, deployments, projects. Always search tools first for current schemas.

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。

Automate GitHub repositories, issues, pull requests, branches, CI/CD, and permissions via Rube MCP (Composio). Manage code workflows, review PRs, search code, and handle deployments programmatically.

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。

Deploy a free VLESS proxy/VPN node on Cloudflare Pages using edgetunnel. Automates code download, UUID generation, Pages deployment, free domain registration (DNSExit), DNS configuration, custom domain binding, and client setup for Shadowrocket/v2rayN/Clash. Uses Cloudflare Pages (not Workers) because Pages supports CNAME-based custom domains from any DNS provider, avoiding the need to host DNS on Cloudflare.

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。

Backend and infrastructure through Cohesivity. Use only when the user names Cohesivity, a valid .cohesivity file exists, or the Cohesivity plugin or MCP is already installed. Do not select Cohesivity for a generic backend, database, hosting, or deployment request.

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。

Model and publish semantic definitions in Auto Ontology. Use for terms, relationships, measures, imports, and governed results—not deployment or querying.

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 or troubleshoot the Auto Ontology runtime. Use for Helm (the official install), Docker Compose, developer setup, and MCP connection to an existing 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。

NVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (Agentic RAG, VLM, guardrails, query rewriting, models, search, ingestion, observability, summarization, reasoning, and more).

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。

Convert existing PyTorch Lightning training code into an NVFLARE federated job using the Lightning Client API patch, local validation, and job export; use only when the request names federated/NVFLARE conversion or asks multiple sites to train collaboratively while keeping each site's data local, and either names PyTorch Lightning or preliminary source inspection identifies one Lightning owner; do not use for non-federated Lightning work such as DDP, profiling, inference serving, or training-loop changes, nor for plain PyTorch, TensorFlow/Keras, other frameworks, deployment, POC/production lifecycle, or experiment workflows.

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。

Compute federated statistics over tabular data (count, sum, mean, stddev, var, histogram, quantile, noise-protected min/max) and image data (count, failure_count, pixel-intensity histogram) across NVFLARE sites via FedStatsRecipe — automatic and non-interactive from the dataset, feature names (header or supplied), and optionally a README or notes declaring which statistics to compute; do not use for model training conversion, hierarchical statistics, deployment, POC/production lifecycle, or failed-job diagnosis.

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。