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概要と使いどころ
Implement GitOps workflows with ArgoCD and Flux for automated, declarative Kubernetes deployments with continuous reconciliation. Use when implementing GitOps practices, automating Kubernetes deployments, or setting up declarative infrastructure 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。
Skill converted from mcp-deploy-manage-agents.prompt.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。
Create and synchronize prompt-based AI agents directly within Azure AI Foundry via REST API, from a local JSON manifest. Unlike scaffolding skills that only generate local code, this skill registers agents in the Foundry service itself — making them immediately available for invocation. Use when the user asks to create agents in Foundry, sync, deploy, register, or push agents to Foundry, update agent instructions, or scaffold the manifest and sync script for a new repository. Triggers: 'create agent in foundry', 'sync foundry agents', 'deploy agents to foundry', 'register agents in foundry', 'push agents', 'create foundry agent manifest', 'scaffold agent sync'.
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 create, configure, and deploy Azure Static Web Apps using the SWA CLI. Use when deploying static sites to Azure, setting up SWA local development, configuring staticwebapp.config.json, adding Azure Functions APIs to SWA, or setting up GitHub Actions CI/CD for Static Web Apps.
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。
Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.
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 Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services
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。
Scan the portfolio for the highest-leverage AI opportunities and rank where to deploy operating-partner time. Ingests quarterly updates and financials across multiple portfolio companies, identifies quick wins at each, and stacks them into a single ranked action list. Use during quarterly portfolio reviews, annual planning, or when deciding which companies get AI investment first. Triggers on "AI readiness", "AI opportunity scan", "where should we deploy AI", "AI across the portfolio", "AI quick wins", or "which portcos are ready for AI".
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。
Deploys and operates Falco with the modern eBPF driver in Kubernetes and Docker, covering driver selection, Helm installation, output channels, and the built-in ruleset that detects container escape, namespace abuse, privileged mounts, and anomalous syscalls. Use when standing Falco up on a cluster, choosing between the eBPF and kernel-module drivers, routing Falco alerts into a SIEM or Falcosidekick, or upgrading an existing deployment. Keywords: Falco, modern_ebpf, kernel module, Helm, Falcosidekick, runtime security, syscall. Do not use for authoring individual escape rules - use detecting-container-escape-with-falco-rules.
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 host-based intrusion detection systems (HIDS) to monitor endpoint file integrity, system calls, and configuration changes for security violations. Use when deploying OSSEC, Wazuh, or AIDE for endpoint monitoring, building file integrity monitoring (FIM) policies, or meeting compliance requirements for change detection. Activates for requests involving HIDS configuration, file integrity monitoring, OSSEC/Wazuh deployment, or host-based detection.
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。
Deploys and configures Tailscale (or self-hosted Headscale) as a WireGuard-based zero trust mesh VPN, setting up identity-aware ACLs, exit nodes, subnet routers, and MagicDNS for encrypted peer-to-peer connectivity. Use when replacing traditional VPN servers with an identity-authenticated mesh network or enforcing granular per-device access control lists.
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 cloud-native deception across AWS, Azure, and GCP using decoy (honey) resources whose only purpose is to generate a high-fidelity alert the instant an attacker touches them: canary IAM access keys, permission-less decoy users/roles/service principals, honey object-storage buckets, and decoy secrets in Secrets Manager / Key Vault / Secret Manager. Wires detection through CloudTrail + EventBridge, Azure Sentinel honeytoken watchlists + Defender, and GCP Cloud Audit Logs, so any use of a decoy is routed to the SOC with near-zero false positives. Use when protecting cloud accounts and data stores, when an org has only on-prem honeypots and needs cloud coverage, when seeding fake AWS keys to catch credential theft and code-leak exposure, or when detecting cloud reconnaissance and lateral movement. Keywords: cloud deception, canary token AWS, honey S3 bucket, decoy IAM credentials, CloudTrail alert, GuardDuty, Sentinel honeytoken, decoy secret, honey service account, cloud honeypot, breach detection.
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。
Deploys Cloudflare Access with Cloudflare Tunnel for zero trust access to self-hosted apps, configuring identity-aware policies, device posture checks, and WARP client enrollment as a VPN replacement. Use when replacing VPN with Cloudflare One, exposing internal apps without open inbound ports, or securing contractor/third-party access to specific 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。
Deploys deception-based honeytokens in Active Directory including fake privileged accounts with AdminCount=1, fake SPNs for Kerberoasting detection (honeyroasting), decoy GPOs with cpassword traps, and fake BloodHound paths. Monitors Windows Security Event IDs 4769, 4625, 4662, 5136 for honeytoken interaction. Use when implementing AD deception defenses for detecting lateral movement, credential theft, and reconnaissance.
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。
Deploys Palo Alto Networks Prisma Access for SASE-based zero trust network access, configuring GlobalProtect agents, ZTNA Connectors, security policy enforcement, and Strata Cloud Manager integration for unified management. Use when implementing enterprise-grade SASE with integrated ZTNA/SWG/CASB/FWaaS, replacing both VPN and branch firewalls with cloud-delivered security, or integrating ZTNA with an existing Palo Alto NGFW estate.
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 AWS Verified Access to provide VPN-less zero trust network access to internal apps, combining identity trust providers (IAM Identity Center, Okta/OIDC), device posture providers (CrowdStrike, Jamf), Cedar policy authoring, and Terraform deployment. Use when replacing VPN access with ZTNA, writing Cedar access policies, or deploying Verified Access instances, groups, and endpoints across AWS accounts.
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。
Deploys a Software-Defined Perimeter per the CSA v2.0 specification, configuring Single Packet Authorization, mutual TLS, and SDP controller/gateway components to enforce zero trust network access. Use when building or hardening zero trust network architecture, implementing SPA-based "invisible" infrastructure that cloaks services from unauthenticated scanning, or meeting compliance requirements for zero trust network access.
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。
Deploys Cisco Duo multi-factor authentication across enterprise applications, VPN, RDP, and SSH access points, covering Duo Authentication Proxy setup, adaptive authentication policies, device trust assessment, and phishing-resistant WebAuthn/FIDO2 deployment aligned with NIST 800-63B AAL2/AAL3. Use when adding or hardening MFA for remote access, VPN, or privileged logins with Duo.
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 architecture documentation using C4 model Mermaid diagrams. Use when asked to create architecture diagrams, document system architecture, visualize software structure, create C4 diagrams, or generate context/container/component/deployment diagrams. Triggers include "architecture diagram", "C4 diagram", "system context", "container diagram", "component diagram", "deployment diagram", "document architecture", "visualize architecture".
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。
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
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。
Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.
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 cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
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。
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle 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。
Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, 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。
Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation 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。