「sca」の検索結果

Build Holoscan SDK from source via the in-tree ./run script. Use only when published packages don't meet the user's needs.

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Install Holoscan SDK v4.3+ via Conda in a CUDA 13 environment. Use for Conda installs; redirect CUDA 12 hosts to container/wheel.

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Query TradingView market data through the bundled tradingview MCP server without a desktop app or login. Use whenever the user wants a technical analysis readout (RSI, MACD, Bollinger rating, BUY/SELL summary), multi-timeframe alignment (weekly→daily→4h→1h→15m), exchange-wide scans (Bollinger squeeze, volume breakout, consecutive candles, smart volume), top gainers/losers by exchange, futures market data (NQ/ES/CL/GC overview, overnight movers, category snapshots), pre-market / after-hours quotes, a quick options chain or unusual options activity (volume/OI by strike), bulk stock prices, a global market snapshot, strategy backtests (Sharpe, drawdown, win rate), or crypto exchange scans. Triggers include "TA on X", "multi-timeframe read", "BB squeeze scan", "futures movers", "NQ overnight", "pre-market price", "unusual options activity", "backtest RSI on AAPL", and "top gainers on NASDAQ". For options greeks, watchlists, alerts, or chart screenshots use tradingview-reader instead.

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Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters. Covers Day-0 checklist, SKU selection (Automatic vs Standard), networking options (private API server, Azure CNI Overlay, egress configuration), security, and operations (autoscaling, upgrade strategy, cost analysis). WHEN: create AKS environment, provision AKS, enable AKS observability, design AKS networking, choose AKS SKU, secure AKS, optimize AKS, AKS spot nodes, AKS cluster-autoscaler, rightsize AKS pod, pod rightsizing, over-provisioned AKS pod, pod resource requests and limits, Vertical Pod Autoscaler, VPA recommendations.

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Critique a rendered screen's typography — scale usage, readability, consistency, and token compliance. Use when reviewing type on a screen. For defining the scale itself, use `typography-scale` (ui-design).

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Interactive discovery + implementation workflow that gathers requirements through picker-based questions (intent, scope, constraints, preferences), scans the codebase for what it can already infer, then writes an AWS architectural scaffold and implementation directly into the project. Use when the user wants to build a new app, scaffold a project, or expand/refactor an existing one on AWS — anything that calls for a structured discovery flow followed by code changes, not a one-off lookup. Do not use for: factual lookups about AWS Activate / programs / credits, requests for a single copy-paste prompt, non-AWS architectural work, architecture advice/recommendations without code changes (see architect-for-startups), a new idea centered on an AI agent — runtime selection, agentic architecture, or an agent POC (see agent-advisor), or migrating an existing GCP/Azure/Heroku workload or an LLM SDK rewrite (see gcp-to-aws / azure-to-aws / heroku-to-aws / llm-to-bedrock).

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Provisions, scales, and operates Amazon EC2 virtual-machine workloads: instance-type selection (Graviton/Arm64, burstable T credits, GPU, instance store vs EBS), launch templates, Auto Scaling groups (scaling policies, instance refresh, mixed instances, Spot, warm pools, lifecycle hooks), IMDSv2, placement groups, Elastic IPs, AMI lifecycle, and Systems Manager fleet operations (Session Manager, Run Command, Patch Manager). Applies to EC2 instance and fleet questions, InsufficientInstanceCapacity, CPU-credit/surplus charges, IMDSv2 401s, instances stuck in Pending:Wait, ASG not replacing unhealthy instances, status-check failures, SSH refused/timed out, or instances missing as SSM managed nodes. For a single secure instance launch, the launching-ec2-instance-with-best-practices skill is more appropriate; for instance profiles, see setting-up-ec2-instance-profiles; for Image Builder, see amazon-ec2-image-builder. Does NOT cover Lambda, ECS/Fargate, EKS, VPC/ALB/NLB design, or IAM policy authoring.

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Scaffold production-ready AI agents on Google's Agent Development Kit (ADK): ReAct-style single agents, multi-agent orchestration (Sequential/Parallel/Loop), tool wiring, evaluation, and optional Vertex AI Agent Engine deployment. Use when building, scaffolding, or deploying ADK agents on Google Cloud, or when wiring ADK tools and orchestration patterns. Trigger with "build an ADK agent", "scaffold an agent on ADK", or "deploy to Agent Engine".

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Linux privilege escalation playbook. Use when you have low-privilege shell access and need to escalate to root via SUID/SGID binaries, capabilities, cron abuse, kernel exploits, misconfigurations, or credential harvesting on Linux systems.

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Windows local privilege escalation playbook. Use when you have low-privilege shell access on Windows and need to escalate via token abuse, Potato exploits, service misconfigurations, DLL hijacking, UAC bypass, or registry autoruns.

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Container escape playbook. Use when operating inside a Docker container, LXC, or Kubernetes pod and need to escape to the host via privileged mode, capabilities, Docker socket, cgroup abuse, namespace tricks, or runtime vulnerabilities.

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Design scalable distributed systems using structured approaches for load balancing, caching, database scaling, and message queues. Use when the user mentions "system design", "scale this", "high availability", "rate limiter", "design a URL shortener", "design Twitter", "design Uber", "design a news feed", "system design interview", "capacity planning", or "distributed architecture". Also trigger when estimating infrastructure requirements, choosing between microservices and monoliths, or designing for millions of concurrent users. Covers common system designs (TinyURL, feeds, chat) and back-of-the-envelope estimation. For data fundamentals, see ddia-systems. For resilience, see release-it.

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Audit and fix visual hierarchy, spacing, color, and depth in web UIs. Use when the user mentions "my UI looks off" (or amateur/unprofessional), "fix the design", "Tailwind styling", "color palette", "visual hierarchy", "design system", "spacing scale", or "component styling". Also trigger when building consistent design tokens, creating dark mode themes, improving data-visualization clarity, or polishing UI details before launch. Covers grayscale-first workflow, constrained design scales, shadows, and component styling. For typeface selection, see web-typography. For usability audits, see ux-heuristics.

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Start and scale networked products using Andrew Chen's "The Cold Start Problem" framework for network effects. Use when the user mentions "network effects", "chicken and egg", "cold start", "two-sided marketplace", "atomic network", "hard side", "liquidity", "critical mass", "invite-only launch", "how do I get my first users", or "the marketplace has no buyers or sellers". Also trigger when launching a marketplace, social, or collaboration product that is worthless without other users, deciding launch sequencing and seeding tactics, or diagnosing stalled network growth at scale. Covers the five stages: cold start, tipping point, escape velocity, hitting the ceiling, and the moat. For word-of-mouth virality, see contagious. For habit-driven retention, see hooked-ux.

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Direct and produce a short film made entirely in code, in one of the styles of the Lemo-Opuscar library (e.g. Impasto Oil Painting 油画厚涂, Watercolor Brush 水彩笔刷, Chinese Ink Wash 中国水墨, Ukiyo-e 浮世绘, Whiteboard Explainer 白板讲解). Use when the user asks for a video, film, short, promo, explainer or animation (视频、短片、动画、宣传片) in a named style; when they want a film made about their own topic and haven't chosen a style (help them choose); or when they ask which film styles there are. Not for editing or converting existing video files.

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Use when evaluating crypto through a Grayscale-style Crypto Sectors lens: sector taxonomy, FTSE/Grayscale index eligibility, fee/usage fundamentals, sector-share valuation, ETP/trust wrappers, and Zcash-style privacy-as-money theses.

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Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)

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Use Trivy vulnerability scanner in offline mode to discover security vulnerabilities in dependency files. This skill covers setting up offline scanning, executing Trivy against package lock files, and generating JSON vulnerability reports without requiring internet access.

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Guide for mapping common Python libraries and idioms to Scala equivalents. Use when converting Python code that uses standard library modules (json, datetime, os, re, logging) or needs equivalent Scala libraries for HTTP, testing, or async operations.

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Guide for translating Python code to functional Scala style. Use when converting Python code involving higher-order functions, decorators, closures, generators, or when aiming for idiomatic functional Scala with pattern matching, Option handling, and monadic operations.

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Reference guide for translating Python syntax constructs to Scala equivalents. Use when converting Python code to Scala and need mappings for basic syntax elements like variable declarations, control flow, comprehensions, string formatting, and common operators.

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Expert performance engineer specializing in modern observability, application optimization, and scalable system performance. Masters OpenTelemetry, distributed tracing, load testing, multi-tier caching, Core Web Vitals, and performance monitoring. Handles end-to-end optimization, real user monitoring, and scalability patterns. Use PROACTIVELY for performance optimization, observability, or scalability challenges.

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Master enterprise-grade Scala development with functional programming, distributed systems, and big data processing. Expert in Apache Pekko, Akka, Spark, ZIO/Cats Effect, and reactive architectures. Use PROACTIVELY for Scala system design, performance optimization, or enterprise integration.

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Scaffold a graded problem set with sections, problems, worked solutions, and short "why this matters" explainers across analytical, empirical, and coding types. Use when user says "make a problem set on X", "scaffold exercises for this lecture", "create practice problems", "generate homework with a solution key", "build a graded assignment on topic Y". Emits a clean student set plus a separate solution key — NOT for grading submissions or auto-checking student answers.

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。