「selection」の検索結果

Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strategies, migration paths), this skill produces a 1-3-1 format: one clear problem statement, three distinct options with pros/cons, and one concrete recommendation with definition of done and implementation plan. Use when the user asks for a "1-3-1", says "give me options", or needs help choosing between competing approaches.

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 when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

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。

Compresses images to WebP (default) or PNG with automatic tool selection. Use when user asks to "compress image", "optimize image", "convert to webp", or reduce image file size.

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。

Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

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。

Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming 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。

Systematically evaluate architecture decisions, document trade-offs, and select appropriate patterns. This skill should be used when the user asks about 'architecture decision', 'ADR', 'design pattern selection', 'technology choice', or needs to evaluate architectural trade-offs. Keywords: architecture, ADR, patterns, trade-offs, technical debt, quality attributes, decision record.

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 when the user asks to test, verify, interact with, or automate web pages and browsers. Trigger for requests involving Chrome automation, browser testing, web scraping, screenshot capture, element selection, or checking web applications. Also trigger when user mentions "browser tools" explicitly.

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。

Power BI report visualization design prompt for creating effective, user-friendly, and accessible reports with optimal chart selection and layout design.

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 when the user needs to transform business requirements, use case descriptions, or meeting transcripts into a technical Power Platform solution architecture, including component selection and Mermaid.js diagrams.

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。

Use this skill to create CodeTour .tour files — persona-targeted, step-by-step walkthroughs that link to real files and line numbers. Trigger for: "create a tour", "make a code tour", "generate a tour", "onboarding tour", "tour for this PR", "tour for this bug", "RCA tour", "architecture tour", "explain how X works", "vibe check", "PR review tour", "contributor guide", "help someone ramp up", or any request for a structured walkthrough through code. Supports 20 developer personas (new joiner, bug fixer, architect, PR reviewer, vibecoder, security reviewer, and more), all CodeTour step types (file/line, selection, pattern, uri, commands, view), and tour-level fields (ref, isPrimary, nextTour). Works with any repository in any language.

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。

Core principles and golden rules for writing production-quality Ansible automation, covering FQCN requirements, module selection guidance, and execution patterns using uv run.

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。

Download media (images/videos/GIFs/audio) from Twitter/X using gallery-dl. Supports single tweet URLs, user timelines, date filtering, post count limiting, retweet filtering, video quality selection, and various media types. Use when users want to download media from Twitter/X accounts with proxy support (HTTP/SOCKS4/SOCKS5) and cookie authentication. Supports downloading tweet text with links alongside media.

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。

Generate AI design contract (AI-SPEC.md) for phases that involve building AI systems — framework selection, implementation guidance from official docs, and evaluation strategy

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。

Red team engagement planning is the foundational phase that defines scope, objectives, rules of engagement (ROE), threat model selection, and operational timelines before any offensive testing begins.

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。

Configure secure OAuth 2.0 authorization flows including Authorization Code with PKCE, Client Credentials, and Device Authorization Grant. This skill covers flow selection, PKCE implementation, token

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。

AEM Cloud Service expert skill for replication / content distribution. Covers migration from CQ Replicator (com.day.cq.replication.Replicator) and Sling Replication Agent (org.apache.sling.replication.agent.api) to the Sling Distribution API (Distributor + SimpleDistributionRequest). Includes agent selection (publish vs preview), async response handling, author cluster coordination, service-user setup, review checklist, troubleshooting, and common pitfalls.

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。

Analyze content sequences and determine authoring approach (default content vs blocks). Validates block selection and section styling for import/migration to AEM Edge Delivery Services.

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。

Provides advanced UI/UX design intelligence for portfolio and app work, including design system selection, palette and typography guidance, and implementation checks.

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。

Design, critique, or specify honest charts and data visualizations from known data and audience needs. Use for chart selection, misleading-chart audits, accessibility, dashboard visuals, or "make this data easier to understand"; do not use for raw data analysis, PPT deck generation, spreadsheet surgery, or fabricated chart data.

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。

Reduces LLM costs and improves response times through caching, model selection, batching, and prompt optimization. Provides cost breakdowns, latency hotspots, and configuration recommendations. Use for "cost reduction", "performance optimization", "latency improvement", or "efficiency".

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 when a Spark branch or pull request needs final diff hygiene, validation selection, commit scope, remote topology, and readiness checked before publication.

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。

Wi-Fi reconnaissance methodology — adapter selection, monitor mode and packet injection setup, regulatory domain handling, multi-band airspace mapping, hidden SSID discovery, BSSID/ESSID/channel/PMF/encryption fingerprinting, client probe analysis, vendor OUI lookup, war-driving with Kismet/airodump-ng/Wigle, and structured airspace data capture for downstream attacks. Use at the start of any wireless engagement to build the target map before active attacks; covers 2.4 GHz, 5 GHz, and 6 GHz (Wi-Fi 6E) bands and adapter compatibility for each.

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。

Structured OSINT methodology framework: target definition, source selection, collection workflows, data correlation, timeline reconstruction, and reporting. Use to guide systematic OSINT campaigns or teach OSINT methodology.

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。