「deployment」の検索結果

Security hardening and best practices for robotic systems, covering SROS2 DDS security, network segmentation, secrets management, secure boot, and the physical-cyber safety intersection. Use this skill when securing ROS2 communications, configuring DDS encryption and access control, hardening robot onboard computers, managing certificates and credentials, setting up network segmentation for robot fleets, or addressing the unique security challenges where cyber vulnerabilities become physical safety risks. Trigger whenever the user mentions SROS2, DDS security, robot security, robot hardening, ROS2 encryption, ROS2 access control, robot network security, secure robot deployment, robot certificates, keystore generation, robot firewall, e-stop security, safety controller isolation, or IEC 62443 for robotics.

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。

AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python. Use when creating CDK stacks, defining CDK constructs, implementing infrastructure as code, or when the user mentions CDK, CloudFormation, IaC, cdk synth, cdk deploy, or wants to define AWS infrastructure programmatically. Covers CDK app structure, construct patterns, stack composition, and deployment 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。

AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.

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 AWS CloudFormation patterns for Lambda functions, layers, API Gateway integration, event sources, cold start optimization, monitoring, logging, template validation, and deployment workflows. Use when creating Lambda functions with CloudFormation, configuring event sources, implementing cold start optimization, managing layers, integrating with API Gateway, and deploying Lambda infrastructure.

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 AWS SAM bootstrap patterns: generates `template.yaml` and `samconfig.toml` for new projects via `sam init`, creates SAM templates for existing Lambda/CloudFormation code migration, validates build/package/deploy workflows, and configures local testing with `sam local invoke`. Use when the user asks about SAM projects, `sam init`, `sam deploy`, serverless deployments, or needs to bootstrap/migrate Lambda functions with SAM templates.

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 AWS CloudFormation patterns for Amazon RDS databases. Use when creating RDS instances (MySQL, PostgreSQL, Aurora), DB clusters, multi-AZ deployments, parameter groups, subnet groups, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.

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 AWS CloudFormation patterns for VPC foundations, including subnets, route tables, internet and NAT gateways, endpoints, and reusable outputs. Use when creating a new network baseline, segmenting public and private workloads, or preparing CloudFormation networking stacks for application 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。

Provides security review capability for TypeScript/Node.js applications, validates code against XSS, injection, CSRF, JWT/OAuth2 flaws, dependency CVEs, and secrets exposure. Use when performing security audits, before deployment, reviewing authentication/authorization implementations, or ensuring OWASP compliance for Express, NestJS, and Next.js. Triggers on "security review", "check for security issues", "TypeScript 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。

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。

Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model / Vector Search / model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs.

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。

Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: Unity Gateway CRUD and management (databricks-unity-gateway), training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).

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。

Drive the agentkit CLI (aliases agentkit / ak) to scaffold, build, deploy, and manage agents on Volcengine AgentKit from the terminal — runtimes, knowledge bases, memory collections, tools, harnesses, invocation, SSO auth, sandboxes, and evaluation datasets. Use when a user wants to create/deploy an agent project, call or inspect a deployed runtime, manage AgentKit resources, or resolve a CLI/auth/deploy error. Does NOT handle non-AgentKit deployment, general Docker/k8s operations, Volcengine IAM management, or writing agent application code (use veadk-agent-development for that).

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。

Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.

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。

Computes DORA delivery-performance metrics from git and GitHub API. Use when assessing deployment frequency, lead time, or change failure rate.

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。

Standardizes release approvals with GitHub-aware checklists and deployment gates. Use before releasing to production to verify all gates pass.

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。

Configures GitHub Actions CI/CD workflows for testing, linting, and deployment. Use when setting up automation for a Python, Rust, or TypeScript project.

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。

Applies microservices for independent deployment and per-service scaling. Use when teams need autonomous release cycles with distinct capability scaling needs.

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。

Operate and extend GeoNode geospatial content-management deployments, resource APIs, uploads, metadata/catalogues, GeoServer security, harvesting, and administration with explicit service and safety 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。

Use face.evoLVe for high-performance face recognition workflows across alignment, data preparation, PyTorch training, feature verification, and Paddle 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。

Operate FATE 2.2.0 federated-learning deployments, FateFlow Pipeline workflows, local launchers, and component CLI/runtime inspection.

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。

Operate FastReID setup, configuration, datasets, model/inference APIs, training/evaluation workflows, deployment/export paths, and extension projects for person and vehicle re-identification.

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。

Route Flower repository tasks for app authoring, strategies and mods, local simulation and deployment, Flower Datasets and examples, and repository maintenance.

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。

Operate doccano for text annotation, dataset import/export, auto-labeling, deployment, and repo maintenance.

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 python-doctr (docTR) for OCR/KIE inference, document IO/export, model customization, datasets/training/evaluation, CLI helpers, deployment, and 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。