「sca」の検索結果

Designs multiscale chunking for RAG by embedding small units for retrieval precision and returning larger context for synthesis. Use when fixed-size chunks either lose surrounding context or dilute relevance in long documents, manuals, filings, or codebases. Covers sentence-window and parent-child or auto-merging strategies, base chunk sizing, and evaluation. Do not use when a single chunk scale already meets retrieval and generation needs.

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Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

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Scan stocks for bullish trends using technical indicators (SMA, RSI, MACD, ADX). Use when user asks to scan for bullish stocks, find trending stocks, or rank symbols by momentum.

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

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Assess whether to escalate models. Use when evaluating reasoning depth.

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Guides the Privacy Threshold Analysis screening process to determine whether a full DPIA is required. Provides a quick-screen questionnaire, threshold criteria based on WP248rev.01, escalation triggers, and documentation requirements. Activate when evaluating new processing activities, system changes, or procurement decisions. Keywords: PTA, privacy threshold analysis, DPIA screening, quick-screen, threshold criteria, WP248, escalation triggers.

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Links RoPA entries to Data Protection Impact Assessments and lawful basis assessments. Covers cross-reference systems, dependency tracking, and update cascade triggers between RoPA, DPIA register, and lawful basis documentation. Activate for RoPA-DPIA link, cross-reference, dependency tracking, impact assessment linkage, cascade updates.

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Builds a comprehensive breach response team playbook defining CSIRT and privacy team structure with named roles (incident commander, legal counsel, communications, IT forensics, DPO), escalation matrices, communication templates, pre-negotiated vendor contacts, and regulatory authority contacts organized by jurisdiction. Keywords: breach response playbook, CSIRT, incident response team, escalation matrix, communication templates, vendor contacts.

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Implements automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie. Covers scanning schedules, accuracy tuning, false positive management, and integration patterns. Keywords: data discovery, PII scanning, Purview, BigID, Macie, OneTrust, automated classification, data cataloging.

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Applies the engineering and research philosophies of Jeff Dean, Chief Scientist at Google DeepMind and Google Research. Reach for this skill whenever you are designing large-scale distributed systems, optimizing latency and energy efficiency, or making architectural decisions about machine learning infrastructure. It should trigger automatically for topics involving hardware-ML co-design, model distillation, sparse activation, massively multi-task models, or scaling systems by 5x to 10x. Use this skill to evaluate system bottlenecks, transition from specialized to unified models, and optimize experimental velocity. Apply his mental models to avoid premature 100x scaling and to treat AI models as reasoning engines rather than memorization databases.

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Upscale images and video 2x to 4K/8K on the user's GPU through Guaardvark (Real-ESRGAN, HAT-L, SwinIR, two-pass). Use when the user asks to enlarge, sharpen, restore, or upscale a picture, a batch of pictures, or a video.

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Scaffold end-to-end testing infrastructure. Use when the user says "e2e scaffold" or "set up e2e testing"

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按 scarletkc 本人的自然表达习惯代写、改写、润色和翻译文本，适用于推文、评论、聊天消息、模型或工具体验文、项目介绍、GitHub 文本和正式通信。用户要求撰写可直接使用的成稿、去除 AI 腔，或在翻译中保留本人语气和立场时使用，无需明确点名本 skill。单纯的事实问答、技术分析、代码审查和任务讨论不触发。

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Scan project dependencies for known vulnerabilities, generate software bills of materials, and enforce license compliance across the software supply chain. Use when the user requests dependency scanning or provides relevant inputs for this workflow.

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Use when enhancing, upscaling, restoring, denoising, sharpening, deinterlacing, stabilizing, motion-deblurring, colorizing, SDR-to-HDR converting, or frame-rate/slow-motion converting video (and secondarily images) with Topaz Labs — whether cleaning up AI-generated video, compressed UGC, or old archival footage, or building a generate-low-res-then-upscale delivery pipeline. Covers choosing the right named Topaz model for a specific footage problem, setting parameters, driving the Topaz Platform (Video/Image) REST API's async job lifecycle, judging when enhancement helps versus when it introduces artifacts (over-smoothing, temporal shimmer, face hallucination), and QA of enhanced output. Do not use for originating new video content, non-Topaz upscalers, or purely editorial cuts.

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Research and evaluate u.s. fiscal data with current authoritative sources, provenance, and explicit evidence gaps. Use when the user asks for u.s. fiscal data findings or verification.

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Seed a fresh (greenfield) repository with the PRD-Driven Context Engineering scaffold — PRD.md, SoT/ knowledge files, EPIC templates, domain-profile config, and agent MEMORY starters. Invoked as /prd-ce:init when the plugin is installed. The framework itself (skills, agents, hooks, scripts) ships LIVE in the plugin; this skill only plants the consumer-owned files the plugin cannot carry as behavior. Triggers on "/prd-ce:init", "set up PRD lifecycle here", "scaffold a new PRD-CE project", "initialize the methodology". Outputs a seeded scaffold + a verification report.

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Map the competitive landscape before positioning your product for PRD v0.2 Market Definition. Triggers on completing v0.1 Spark, analyzing competitors, researching market, or requests like "competitive analysis", "who else solves this", "market landscape", "what alternatives exist", "competitor research", "feature comparison". Outputs CFD- entries for competitive intelligence and BR- entries for positioning rules.

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Launch and manage Amazon EC2 instances for scalable compute. Configure AMIs, security groups, key pairs, and EBS volumes. Set up auto-scaling groups for high availability and cost optimization across availability zones.

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Paul Graham's founder mode. When to delegate vs. dive deep. Maintaining velocity as you scale. Avoiding "professional CEO" traps. The operating mode that built Apple, Tesla, and every YC unicorn. Founder mode isn't micromanagement - it's having the conviction and context to make decisive moves that employees can't. It's staying in the arena when conventional wisdom says to step back. Use when "founder mode, should i delegate, staying close to work, micromanagement, when to step back, professional ceo, manager vs founder, skip level, how involved should i be, letting go of control, scaling as founder, founder burnout, founder, leadership, delegation, management, scaling, operational" mentioned.

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Before product-market fit, your job is to learn as fast as possible. That means doing things that won't scale - manual onboarding, concierge service, recruiting users one by one. Paul Graham's "Do Things That Don't Scale" is the bible here. This skill synthesizes the hustle playbook that creates escape velocity. Use when "keywords, contexts, " mentioned.

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The art and science of creating AI-powered digital presenters, avatars, and synthetic spokespersons. This skill covers HeyGen, Synthesia, D-ID, Tavus, and the emerging landscape of photorealistic AI humans that can speak any script in any language. Digital humans aren't replacing human presenters—they're enabling scale that humans can't achieve. A product demo in 50 languages. Personalized video messages for thousands of customers. 24/7 customer support with a friendly face. Training videos that can be updated without reshoots. The practitioners of this skill understand both the power and the responsibility. They know when digital humans enhance experiences and when they feel uncanny. They navigate the ethics of synthetic media thoughtfully. They create AI presenters that feel helpful, not deceptive. Use when "digital human, AI avatar, AI presenter, HeyGen, Synthesia, D-ID, Tavus, synthetic, talking head AI, AI spokesperson, personalized video, video at scale, multilingual video, AI actor, digital-humans, avatar, ai-presenter, synthesia, heygen, d-id, synthetic-media, personalization, multilingual" mentioned.

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The orchestration layer for AI-native creative production. This skill coordinates multiple AI tools—video, image, audio, digital humans, effects—into cohesive campaigns, productions, and creative systems. As AI tools proliferate, the challenge shifts from "can we create this?" to "how do we orchestrate these capabilities into something coherent?" The AI Creative Director thinks in systems, not tools. In pipelines, not one-offs. In brand consistency across AI-generated assets. This is where creative vision meets technical orchestration. The AI Creative Director doesn't just use AI tools—they compose them into creative instruments that produce at scales and speeds previously impossible. Use when "AI creative director, orchestrate AI, AI campaign, multi-tool, AI workflow, AI pipeline, coordinate AI, AI production, AI creative system, full AI production, AI at scale, orchestration, creative-direction, ai-production, workflow, pipeline, multi-tool, scale, quality-control" mentioned.

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Template-based page generation at scale for SEO. Data-driven content, internal linking architecture, and automated optimization for large-scale content operations. Use when the user asks about programmatic SEO, pSEO, scaled content, template pages, or automated page generation.

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。