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概要と使いどころ
Build alternative browser engines using BrowserEngineKit. Use when developing a non-WebKit browser engine for iOS/iPadOS in supported regions, managing web content/rendering/networking extension processes, configuring GPU and networking process capabilities, checking alternative-engine device eligibility, or reviewing BrowserEngineKit entitlements and Info.plist setup.
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。
PDN DC voltage/current analysis, IR drop, design rule checking, and multi-net batch analysis on imported PCB layouts. TRIGGER: user asks about power integrity, PDN analysis, IR drop, voltage distribution, current density, power nets, or design rule checking on a PCB. Invoke BEFORE writing code — the PDN API chain is specialized and non-obvious. SKIP: importing a PCB file (use matlab-read-pcb-layout), EM field/S-parameter extraction (use matlab-analyze-em), material/stackup setup only (use matlab-manage-pcb-material), transmission line design (use matlab-design-pcb-transmission-line).
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。
End-to-end finite element analysis in MATLAB PDE Toolbox — geometry creation, model setup, solve, and post-processing in one skill. Use when building geometry from primitives or file import, setting up femodel with BCs/loads/materials, solving thermal/structural/EM problems, and extracting or visualizing results. Covers fegeometry, multicuboid, multicylinder, multisphere, decsg, boolean ops, mesh generation, femodel, all AnalysisTypes (thermalSteady, thermalTransient, structuralStatic, structuralTransient, structuralModal, structuralFrequency, electrostatic, magnetostatic, dcConduction, harmonic EM), materialProperties, faceBC, faceLoad, cellLoad, vertexLoad, solve, interpolation, von Mises stress, principal stress, reaction forces, heat flux, pdeplot3D visualization. Triggers on: PDE Toolbox, finite element, FEA, thermal analysis, structural analysis, electromagnetic analysis, femodel, mesh, boundary conditions, stress, displacement, heat transfer, post-processing.
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 this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).
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 iptables, nftables, and cloud firewalls. Implement network segmentation and traffic filtering. Use when securing network perimeters or implementing security zones.
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。
Perform basic penetration testing and security assessments. Use reconnaissance, vulnerability discovery, and exploitation techniques. Use when validating security controls or assessing system security.
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 VPCs, firewall rules, and Cloud NAT. Implement shared VPC and private service connect. Use when designing GCP network 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。
Customize Kubernetes manifests without templating using Kustomize. Create base configurations with environment overlays, manage configuration variants, and patch resources declaratively. Use when managing Kubernetes configurations across multiple environments without Helm.
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 GPU-backed Kubernetes clusters for AI inference and training with scheduling, autoscaling, node health, MIG partitioning, and cost controls.
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 ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.
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 MySQL-compatible databases on PlanetScale with branching workflows, safe migrations, and production rollouts.
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。
Implement GitOps with ArgoCD for declarative Kubernetes deployments. Configure applications, manage sync policies, implement progressive delivery, and automate deployments from Git repositories. Use when implementing GitOps workflows or continuous deployment to Kubernetes.
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, manage, and deploy Helm charts for Kubernetes package management. Build reusable chart templates, manage releases, configure values, and use Helm repositories. Use when packaging Kubernetes applications or managing K8s deployments with Helm.
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。
When the user wants to run display, banner, or ad network campaigns. Also use when the user mentions "display ads," "banner ads," "ad network," "ad alliance," "programmatic display," "native ads," or "retargeting display." For strategy, use paid-ads-strategy.
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 Godot 4 game goes online or co-op: host and join with ENet, WebSocket for a web build, RPCs (@rpc, rpc_id, any_peer), MultiplayerSpawner and MultiplayerSynchronizer, player authority, lobbies and login, a dedicated or headless server export, lag, rubber-banding, prediction or netfox, cheating, bandwidth, Steam, Nakama or WebRTC. Symptoms: players do not appear on the client, 'rpc node checksum failed', 'node not found' on an RPC, desync, the server build is huge, a client never connects.
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 turning a song into a kinetic-typography or motion-design music video, lyric video, visualizer or K-pop style MV with Scenario, landscape or vertical 9:16 for TikTok, Reels and Shorts: a generated subject sheet, style frames and footage, plus a deterministic JavaScript/three.js engine for lyric type, mattes and tracked graphics timed to sung onsets. Also for revising, recutting or cutting teasers from such a video.
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。
How to read experiment results without fooling yourself. Confidence intervals, p-values, multiple testing, sequential testing, CUPED, heterogeneous treatment effects, ratio metrics, network effects, dashboard reconciliation, and the interpretation failures that produce confidently wrong shipping decisions. Use this skill whenever the user is reading a finished experiment result panel and about to make a ship, kill, or iterate decision, or when an experiment number does not match the dashboard number. Triggers on read experiment results, result panel, ship or kill decision, p-value, confidence interval, statistical significance, multiple testing, peeking, sequential testing, CUPED, variance reduction, heterogeneous treatment effects, ratio metric, network effects, inconclusive test, experiment versus dashboard mismatch. Use `experiment-design` instead when the test has not run yet and the question is hypothesis, sample size, duration, or what to test.
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。
Designing interactive calculators (ROI calculators, pricing estimators, savings projections, mortgage calculators, custom assessments) that deliver real decision-support value while serving as lead magnets and qualified-traffic generators. Honest about vanity-calculator (no real value), lead-trap (hides the answer behind email), and transparent-decision-tool (gives the result and earns the email through tiered value) patterns. Triggers on calculator, ROI calculator, pricing estimator, savings calculator, custom calculator, interactive tool, decision tool, financial calculator. Also triggers when an audience needs a calculation-driven lead magnet, when a vanity calculator is producing leads but no qualified ones, or when a calculator is being scoped for the first time.
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。
Debug Kubernetes pods, nodes, and workloads. Use when pods are failing, containers crash, nodes are unhealthy, or users mention debugging, troubleshooting, or diagnosing Kubernetes issues.
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 kind (Kubernetes IN Docker) local clusters. Use when creating, testing, or developing with local Kubernetes clusters in Docker containers.
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。
Browser automation for Kubernetes dashboards and web UIs. Use when interacting with Kubernetes Dashboard, Grafana, ArgoCD UI, or other web interfaces. Requires MCP_BROWSER_ENABLED=true.
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。
Virtual machine management with KubeVirt on Kubernetes. Use when creating, managing, or troubleshooting VMs running on Kubernetes clusters.
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。
Respond to Kubernetes incidents with runbooks and diagnostics. Use for outages, pod failures, node issues, network problems, and emergency response.
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。
Expert PCI DSS compliance advisor covering PCI DSS v4.0.1 (current) and v4.0. Use this skill whenever a user asks about PCI DSS, payment card security, cardholder data protection, CDE scoping, SAQ types (A, A-EP, B, B-IP, C, C-VT, P2PE, D), ROC, AOC, QSA assessments, ASV scans, merchant levels, service provider levels, network segmentation, penetration testing, tokenisation, encryption of PAN data, or any of the 12 PCI DSS requirements. Also trigger for questions like "are we PCI compliant?", "how do I scope my CDE?", "which SAQ applies to us?", "what changed in PCI DSS v4.0?", "how do I prepare for a QSA audit?", or any request involving payment data security, cardholder data environment, or PCI certification readiness.
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。