1.3천건 · 관련도순
Use when working with Aws Proton — aWS Proton service and environment template management. Covers environment templates, service templates, service instances, pipeline management, component inspection, and template sync configuration. Use when managing Proton environments, deploying services from templates, inspecting provisioned resources, or auditing template versions.
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Use when working with Gcp Cloud Run — google Cloud Run service management, revision traffic splitting, scaling configuration, concurrency tuning, and container diagnostics via gcloud CLI.
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Use when working with Aws Lambda — aWS Lambda function analysis, invocation metrics, cold start analysis, layer management, and concurrency tracking. Covers function configuration review, error rate analysis, duration percentiles, provisioned concurrency utilization, and deployment package optimization.
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Use when working with Azure Container Apps — azure Container Apps revision management, scaling rules, Dapr integration, ingress configuration, and environment health via Azure CLI.
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Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
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Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.
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Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
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NVIDIA DeepStream SDK development guidance for Python pyservicemaker pipelines, video analytics, TensorRT integration, and streaming inference workflows.
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Data scientist v3 — ML, deep learning, NLP, CV, experimentation, MLOps
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Creative director v2 — brand vision, campaigns, team leadership, client presentations, storytelling
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Expert en budgétisation et prévisions (FP&A, variance analysis, rolling forecasts, DZ)
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Expert en agents avec boucle humaine (workflows d'approbation, escalade, intégration feedback, supervision)
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Navigate the gap between what you can see and what you can make. The Ira Glass gap. Use when a designer is frustrated by the distance between their taste and their output, when mentoring someone through a creative plateau, or when someone is considering quitting because their work doesn't match their vision.
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Official skill for XcodeBuildMCP. Use when doing iOS/macOS/watchOS/tvOS/visionOS work (build, test, run, debug, log, UI automation).
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Official skill for the XcodeBuildMCP CLI. Use when doing iOS/macOS/watchOS/tvOS/visionOS work (build, test, run, debug, log, UI automation).
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Set up bundle IDs, capabilities, signing certificates, provisioning profiles, and encrypted signing sync with the asc cli. Use when onboarding a new app, rotating signing assets, or sharing them across a team.
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Xcode 26 and Apple platform update notes with a focus on Liquid Glass, Foundation Models Framework, and Apple Intelligence. Use when asked to explain, summarize, compare, or implement Xcode 26 era framework changes across SwiftUI, UIKit, AppKit, Foundation, App Intents, StoreKit, MapKit, WidgetKit, visionOS, or Swift language features.
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Create multiplayer spatial board games using TabletopKit on visionOS. Use when building tabletop game experiences with boards, pieces, cards, and dice, managing player seats and turns, synchronizing game state over FaceTime with Group Activities, rendering game elements with RealityKit, or implementing piece snapping and physics on a virtual table surface.
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Build shared real-time experiences using GroupActivities and SharePlay. Use when implementing shared media playback, collaborative app features, synchronized game state, or any FaceTime/iMessage-integrated group activity on iOS, macOS, tvOS, or visionOS.
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Pixel-based motion and UI change analysis from frame sequences or screenshots using computer vision and visual comparison. Use when `.transition` or visual effects are not observable via MotionEyes logs, when you only have screenshots/video, when you need regression diffs across builds, or when you need to summarize on-screen changes without instrumentation. Do not use when MotionEyes traces are available and you need precise timing/values; use `motioneyes-animation-debug` instead.
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Tokenize, tag, and analyze natural language text using Apple's NaturalLanguage framework and translate between languages with the Translation framework. Use when adding language identification, sentiment analysis, named entity recognition, part-of-speech tagging, text embeddings, or in-app translation to iOS/macOS/visionOS apps.
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Add Apple Pencil drawing, canvas views, tool pickers, and ink serialization using PencilKit. Use when building drawing apps, annotation features, handwriting capture, signature fields, or any Apple Pencil-powered experience on iOS/iPadOS/visionOS.
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Review resumes, cover letters, application packages, and evidence maps from an HR perspective. Use when the user asks for 자기소개서 리뷰, 자소서 검토, HR 검토, 제출 전 점검, unsupported-claim detection, company-name residue checks, or risk-focused revision advice before applying.
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Guide for choosing models in Tinker — available model families, model types (base, instruction, reasoning, hybrid, vision), architecture (dense vs MoE), and how to match renderers to models. Use when the user asks which model to use, what models are available, or how to pick a model for their task.
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