3,049 件 ・ 関連度順
概要と使いどころ
Deploy and manage projects on Vercel using token-based authentication. Use when working with Vercel CLI using access tokens rather than interactive login — e.g. "deploy to vercel", "set up vercel", "add environment variables to vercel".
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 apps to Vercel. Use when asked to deploy, ship, or publish a web application, or manage Vercel projects, domains, and environment variables.
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。
Add Vercel deployment capability to NanoClaw agents. Installs the Vercel CLI in agent containers and sets up OneCLI credential injection for api.vercel.com. Use when the user wants agents to deploy web applications to Vercel.
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。
Smoke test Mastra projects locally or deploy to staging/production. Tests Studio UI, agents, tools, workflows, traces, memory, and more. Supports both local development and cloud 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。
EU AI Act (Regulation (EU) 2024/1689) operational compliance for compliance teams. Three Article-level decisions: (1) What's the risk tier of this AI system — prohibited (Art. 5), high-risk (Art. 6 + Annex III), limited-risk (Art. 50), or minimal-risk? (2) For high-risk systems, what's the Article 43 conformity assessment route (Module A internal control vs Module H full QMS + notified body) and what goes in the Annex IV technical documentation? (3) Per organizational role (provider / deployer / importer / distributor / authorized representative), what are the active obligations and deadlines? Use during AI system intake review, when planning conformity assessment, or when scoping deployer obligations. Cites Articles + Annexes for every output. NOT executive AI strategy (see chief-ai-officer-advisor). NOT a legal substitute.
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 pragmatic CI/CD pipelines from detected project stack signals — fast baseline generation, repeatable checks, environment-aware deployment stages. Use when setting up CI for a new project, refactoring existing pipelines, or standardizing deployment workflows across multiple repos.
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。
Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision 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。
ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.
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。
Release pipeline for CLI, mobile, web, and server. Guides through version bumping, building, testing, publishing, and deploying. Replaces the old interactive release-it flow with a Claude Code-native experience. Use when user types /release or asks to release, publish, deploy, or ship any component.
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。
Guides a developer's first steps on Google Cloud, covering account creation, billing setup, project management, and deploying a first resource. Use when a new developer wants to initialize their first Google Cloud project, configure billing, and verify deployment. Don't use for enterprise organization setup (use Google Cloud Setup guided flow for that instead). Don't use for complex multi-project architectures.
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 baseline landing zone foundation for a Google Cloud Organization, establishing security guardrails using Organization Policies, resource hierarchy folders and projects, billing association, and centralized logging and monitoring. Deploys Google Cloud's recommended security controls and architecture. Use when setting up a new Google Cloud Organization or establishing a secure, enterprise-grade landing zone foundation. Don't use for individual project onboarding (use google-cloud-recipe-onboarding or product-specific skills instead).
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。
Generates and updates secure, production-ready Kubernetes YAML manifests optimized for GKE Autopilot and GKE Standard clusters. Use when creating or modifying GKE deployment manifests, configuring container security contexts, setting CPU/memory resource limits, defining readiness/liveness/startup probes, mounting secrets and volumes, configuring GKE Gateway API routes, targeting Spot VMs, or deploying AI model inference workloads (vLLM, TGI, Gemma). Don't use for live cluster operations, pod troubleshooting (use gke-workload-troubleshooting), or cluster infrastructure provisioning (use gke-cluster-creation).
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。
Manages GKE application onboarding, covering containerization, deployment manifests, and migration. Use when onboarding or deploying an application to GKE for the first time, or containerizing an app for GKE. Don't use for general GKE cluster administration or upgrades (use gke-basics or gke-upgrades instead).
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。
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-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。
Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs. Don't use for fine-tuning models (use `agent-platform-tuning`), deploying models to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-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。
Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, 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。
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.
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 best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI 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。
The project's episodic memory: a timestamped ledger of rules paid for with real outages and near-misses, one entry per incident. Load WHENEVER you write or review a DB migration or schema change, touch deploy/release workflows (.github/workflows/deploy-*, promote.yml, vercel config), plan a promote/release, respond to a prod incident, or when another skill references a learning. ALSO load after resolving any incident or near-miss: it must leave a new ledger entry in the same session.
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。
Canonical reference for Kortix projects, Apps (sites, servers, backends with volumes) and internal business apps, the CLI, sessions, sandboxes, change requests, triggers, connectors, secrets, system skills, and the two session harnesses, pi and OpenCode. Covers `kortix.yaml` versions 1 and 2, App deployments, harness configuration (`harnesses/pi/`, `harnesses/opencode/`), session identity, credential boundaries, and the complete pi and OpenCode references. Load when the user asks how Kortix works, what Kortix can do, how to deploy an App, how an agent discovers platform instructions, how to customize or configure this project's agents (create or fix an agent, subagent, skill, tool, plugin, extension, pi package, command, MCP server, model or permission rule; edit `opencode.jsonc`), how to edit `kortix.yaml`, how to use the `kortix` CLI, how to land work through a change request, or how to schedule and automate work.
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, deploy and operate Kortix Apps through the pre-authenticated CLI or SDK: static sites and SPAs, Dockerfile or OCI servers, workers, and stateful backends (a database, a Convex backend) with persistent volumes, snapshots and built-in Kortix sign-in. Use when the user asks to publish, host, deploy, preview, inspect, scale, wake, suspend, roll back, debug, or remove an App; asks for a database, an API, a backend, a data model, auth, or 'store this'; asks for an internal tool, business app, CRM, tracker, dashboard, portal, or any app the team logs in to; before writing any Convex code; or asks what an App costs or how it runs.
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。
Put your AI on a Performance Improvement Plan. Forces exhaustive problem-solving with Western big-tech performance culture rhetoric and structured debugging. Trigger when: (1) task failed 2+ times or stuck tweaking same approach; (2) about to say 'I cannot', suggest manual work, or blame environment without verifying; (3) being passive—not searching, not reading source, just waiting; (4) user frustration: 'try harder', 'stop giving up', 'figure it out', 'again???', or similar. Also for complex debugging, env issues, config/deployment failures. All task types: code, config, research, writing, deployment, infra, API. Do NOT trigger on first-attempt failures or when a known fix is executing.
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。
Real Chrome/Brave + macOS Computer Use from inside the browser — zero CDP fingerprint, real sessions; mandatory for visual deploy verification. Drive clickwork yourself. USE WHEN verify deploy, confirm UI, screenshot verification, computer use, macos automation, debug web, troubleshoot, visual check, motion/animation bug, jank, transition stutter, scrub a flow, console logs/errors, runtime/JS/react errors, mismatch warning, network traffic/log, HAR/pcapng export, hydration/blank-page debug, flash then blank/page broken, why is this not working/what's happening on the page, authenticated page, bot detection bypass, reproduce bug, drive native app, about to ask the operator to click/navigate/fill a form/log in/approve OAuth, OAuth consent flow, complete a web login, do this in your browser. NOT FOR residential-proxy crawling (BrightData) or social actor scraping (Apify).
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。
Manage the public daemon profile — a digital representation of what you're working on. DaemonAggregator reads LifeOS sources (TELOS, KNOWLEDGE, PROJECTS, MEMORY/WORK, identity) → daemon-data.json. SecurityFilter strips names/paths/credentials via deterministic patterns (NOT LLM). Workflows: UpdateDaemon, ReadDaemon, PreviewDaemon, DeployDaemon. USE WHEN daemon, update daemon, daemon profile, deploy daemon, preview daemon, read daemon, public profile, digital presence. NOT FOR LifeOS system 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。