データ分析、機械学習、LLMアプリ、プロンプト設計（8,722 件）
概要と使いどころ
Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using best practices.
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。
Finalize prompt file using the role of an AI agent to polish the prompt for the end user.
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 whenever the user wants to build scroll animations, scroll effects, parallax, scroll-triggered reveals, pinned sections, horizontal scroll, text animations, or any motion tied to scroll position — in vanilla JS, React, or Next.js. Covers GSAP ScrollTrigger (pinning, scrubbing, snapping, timelines, horizontal scroll, ScrollSmoother, matchMedia) and Framer Motion / Motion v12 (useScroll, useTransform, useSpring, whileInView, variants). Use this skill even if the user just says "animate on scroll", "fade in as I scroll", "make it scroll like Apple", "parallax effect", "sticky section", "scroll progress bar", or "entrance animation". Also triggers for Copilot prompt patterns for GSAP or Framer Motion code generation. Pairs with the premium-frontend-ui skill for creative philosophy and design-level polish.
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。
Comprehensive technology-agnostic prompt for analyzing and documenting project folder structures. Auto-detects project types (.NET, Java, React, Angular, Python, Node.js, Flutter), generates detailed blueprints with visualization options, naming conventions, file placement patterns, and extension templates for maintaining consistent code organization across diverse technology stacks.
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。
Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
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。
Creates structured bug reports for defects found during Oracle-to-PostgreSQL migration. Use when documenting behavioral differences between Oracle and PostgreSQL as actionable bug reports with severity, root cause, and remediation steps.
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 a systematic diagnostic scan of an AI workflow across 5 quality dimensions — prompt quality, context efficiency, tool health, architecture fitness, and safety — producing a scored report with prioritized remediation actions.
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。
Ready-to-use prompts for comprehensive .NET framework upgrade analysis and execution
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。
Pre-breach impact analysis: inventories sensitive data (PII, PHI, PCI-DSS, credentials), traces data flows, scores exposure vectors, and produces a regulatory blast radius report with fine ranges sourced verbatim from GDPR Art. 83, CCPA § 1798.155(a), and HIPAA 45 CFR § 160.404. Cost benchmarks from IBM Cost of a Data Breach Report (annually updated). All citations in references/SOURCES.md for verification. Use when asked: "assess breach impact", "what data could be exposed", "calculate blast radius", "data exposure analysis", "how bad would a breach be", "quantify data risk", "sensitive data inventory", "data flow security audit", "pre-breach assessment", "worst-case breach scenario", "breach readiness", "data risk report", "/data-breach-blast-radius". For any stack handling user data, health records, or financial information. Output labels law-sourced figures (exact) vs heuristic estimates (planning only). Does not replace legal counsel.
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。
Discovers all projects in a .NET solution, classifies each for Oracle-to-PostgreSQL migration eligibility, and produces a persistent master migration plan. Use when starting a multi-project Oracle-to-PostgreSQL migration, creating a migration inventory, or assessing which .NET projects contain Oracle dependencies.
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。
Prompt for creating detailed feature implementation plans, following Epoch monorepo structure.
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。
Prompt for creating an Epic Product Requirements Document (PRD) for a new epic. This PRD will be used as input for generating a technical architecture specification.
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。
Convert a text-based document to markdown following instructions from prompt, or if a documented option is passed, follow the instructions for that option.
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。
Prompt for creating Product Requirements Documents (PRDs) for new features, based on an Epic.
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。
Prompt and workflow for generating conventional commit messages using a structured XML format. Guides users to create standardized, descriptive commit messages in line with the Conventional Commits specification, including instructions, examples, and validation.
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。
Prompt for creating the high-level technical architecture for an Epic, based on a Product Requirements Document.
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。
Prompt for generating an AGENTS.md file for a repository
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。
Interactive prompt refinement workflow: interrogates scope, deliverables, constraints; copies final markdown to clipboard; never writes code. Requires the Joyride extension.
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。
Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.
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Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.
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。
Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
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。
Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.
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。
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
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。
LLM Wiki — persistent markdown knowledge base that compounds across sessions (Karpathy model)
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。