1,155 件 ・ 関連度順
概要と使いどころ
Use when working with Bmc Helix — bMC Helix ITSM platform management covering incident management, problem management, change management, and CMDB operations. Use when creating or updating incidents with categorization and assignment, managing problem investigation workflows, processing change requests through CAB approval, or querying CMDB for infrastructure configuration items and relationships.
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。
Write, configure, and deploy Netlify serverless functions in TypeScript, JavaScript, or Go. Use this when adding an API endpoint or backend route, adding a contact form handler, wiring auth or Identity signup/login hooks, building streaming or AI-proxy responses, scheduling cron jobs, running long background jobs (batch processing/scraping), reacting to deploy or form events, setting up rate limiting or region/memory config, or reading environment variables and secrets inside a function. Covers file locations, the Request/Context/Response handler shape, path routing, config options, and local testing with netlify dev.
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 en eDiscovery (document review, data processing, ESI, litigation hold, TAR)
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 Specialist IA — Expert en vision par ordinateur (YOLO, OpenCV, object detection, OCR, segmentation)
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 en performance des agents IA (optimisation latence, streaming, caching, traitement par lots)
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。
Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.
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。
Draft privacy policies, terms of service, disclaimers, and legal notices. Creates GDPR-compliant texts, cookie policies, and data processing agreements.
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。
Codified expertise for returns authorisation, receipt and inspection, disposition decisions, refund processing, fraud detection, and warranty claims 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。
Expert guidance for LlamaIndex development including RAG applications, vector stores, document processing, query engines, and building production AI applications.
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。
Post-process AI-generated text through the unslop CLI to strip AI writing patterns before publishing
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。
This skill covers the principles for identifying tasks suited to LLM processing, designing effective project architectures, and iterating rapidly using agent-assisted development.
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。
This guide covers essential PDF processing operations using Python libraries and command-line tools. For advanced features, JavaScript libraries, and detailed examples, see reference.md. If you need to fill out a PDF form, read forms.md and follow its instructions.
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。
Best practices for SciPy scientific computing, optimization, signal processing, and statistical analysis in Python
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。
Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads
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。
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data 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。
Alternative Slowbar coffee intelligence — scientific analysis from seed to cup using First Principle + System Thinking. Covers: varieties, terroir, cultivation, disease diagnosis, processing, roasting physics/chemistry, extraction science, brewing, business strategy, storytelling. ALWAYS trigger for coffee-related questions requiring scientific depth. DO NOT trigger for simple recipes or general food questions. Style: science-driven, farmer-first, quality-obsessed.
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。
Collect, download, and verify App Store Connect Analytics reports with asc. Use when users need to discover analytics report requests, select report instances by processing date or granularity, download every segment, or verify downloaded files against Apple's size and MD5 metadata before analysis.
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, review, or improve in-app purchases and subscriptions using StoreKit 2. Use when building paywalls with SubscriptionStoreView or ProductView, processing transactions with Product and Transaction APIs, verifying entitlements, handling purchase flows (consumable, non-consumable, auto-renewable), implementing offer codes or promotional/win-back/introductory offers, managing subscription status and renewal state, setting up StoreKit testing with configuration files, or integrating Family Sharing, Ask to Buy, refund handling, and billing retry logic.
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。
Collect and analyze on-device performance metrics and crash diagnostics using MetricKit. Use when setting up MXMetricManager, handling MXMetricPayload or MXDiagnosticPayload, processing crash/hang/disk-write diagnostics via MXCallStackTree, adding custom signpost metrics, or uploading telemetry to an analytics backend.
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, review, or improve photo picking, camera capture, and media handling in iOS apps using PhotoKit and AVFoundation. Use when working with PhotosPicker, PHPickerViewController, camera capture sessions (AVCaptureSession), photo library access, image loading and display, video recording, or media permissions. Also use when selecting photos from the library, taking pictures, recording video, processing images, or handling photo/camera privacy permissions in Swift apps.
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。
Apply when designing commands, lifecycle steps, or processing loops that run amid crashes, restarts, and retries. Converge to the same end state regardless of partial prior 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。
BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.
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。
BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.
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。
Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous 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。