1.8천건 · 관련도순
Design and create a simulation persona for testing an AI agent. Guides through use case selection, voice and language configuration, behavior prompt crafting, and interruption calibration. Use when user says "create a persona", "design a persona", "set up a test persona", "configure simulation persona", or "build a caller profile".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Set up and connect a new Coval agent for evaluation. Guides through agent type selection, endpoint configuration, system prompt, and default resource attachment. Use when user says "set up an agent", "connect my agent", "create an agent", "add an agent", or "configure agent endpoint".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
THE full UI surface checklist (S1–S18) run on one assigned surface. Use for a named surface check, `/surface-check <surface>`, or a claimed `surface-certification` Work Loop item. NOT for fleet selection or claims (use surface-certification-loop).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Cross-app save-to-notes components and patterns. Use when adding save-to-notes to a feature, wiring overlay note capture, editing features/notes/actions/ or its consumers, or using QuickCaptureButton, SaveToScratchButton, SaveSelectionButton, QuickNotesSheet, CategoryNotesModal, or useOpenQuickNoteSaveOverlay.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Locate CCSPlayerPawn::SetModelFromClass in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CCSPlayerPawn::SetModelFromClass" (symbol CCSPlayerPawn_SetModelFromClass). Shortlist via callers of CBaseModelEntity::SetModel inside pawn model-selection code: this variant takes the pawn's class model. The body resolves a cached model precache handle from the player class and calls SetModel. Distinguish from SetModelFromLoadout by the ABSENCE of loadout/inventory item lookups in the call chain. Trigger: CCSPlayerPawn_SetModelFromClass, CCSPlayerPawn::SetModelFromClass
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Three.js interaction - raycasting, controls, mouse/touch input, object selection. Use when handling user input, implementing click detection, adding camera controls, or creating interactive 3D experiences.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used when the user wants to "synthesize two competing solutions without averaging", "resolve a design/value disagreement by lifting to a higher frame", "止揚 / アウフヘーベン", "矛盾を止揚", "独立案を統合", "平均化せず統合", "contradiction lift", "sublate competing designs", or has two good-but-divergent answers to the same question and wants a higher-order resolution (a selection mechanism) or an honest aporia — not a compromise. NOTE: Use this when two independent solutions diverge and you want to PRESERVE both truth-moments while lifting to a higher frame. Use devils-advocate to stress-test ONE proposal with an assigned opponent, strong-inference to find an unknown cause, and dialectic-loop to validate a claim against a real corpus.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a Spark branch or pull request needs final diff hygiene, validation selection, commit scope, remote topology, and readiness checked before publication.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
PHP Standards Recommendations (PSR) overview knowledge base. Provides comprehensive reference for all accepted PSRs including PSR-1,3,4,6,7,11,12,13,14,15,16,17,18,20. Use for PSR selection decisions and compliance audits.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Эксперт ABM. Используй для account-based marketing, target account selection и personalized campaigns.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use this skill when building or debugging table usage with nuxt-ui-tools as a package consumer. Covers defineTableSchema, useTable, DataList, granular composition, columns, filters, sorting, offset or cursor loading, layouts, summary rows on phones, first-use empty states, actions, selection, accessible row names, context, and URL state with concrete examples.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Power BI report visualization design prompt for creating effective, user-friendly, and accessible reports with optimal chart selection and layout design.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Core principles and golden rules for writing production-quality Ansible automation, covering FQCN requirements, module selection guidance, and execution patterns using uv run.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.