'vertical' 검색 결과

Create sparse vertical posters from themes, articles, or photos, using paper textures, generous empty space, experimental typography, and one accent color. It also analyzes visual references and produces reusable style rules and image generation prompts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn an article into a poster with one visual metaphor
• Create a poster that preserves a photo's recognizable subject
• Extract reusable style rules from reference images

Create a fixed, full-screen WebGL laser background with a thin white core, brand-colored halo, and soft smoke. Adjust beam placement, glow, fog, and rendering cost while keeping foreground content readable and interactive.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a thin laser behind page content
• Matching laser glow and smoke to brand colors
• Adjusting beam position and smoke density

Structure a page with a centered outer frame and inset inner sections. Use visible boundaries, corner markers, backgrounds, borders, and rounded corners to distinguish content levels while keeping spacing consistent and text readable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Establishing visible boundaries for a page
• Separating hero, feature, proof, and CTA sections
• Aligning inset spacing across content blocks

Build or extend an isometric action RPG, viewed from an angled overhead perspective, using web technology. Develop a playable loop covering movement, combat, rewards, progression, and saves, with staged implementation and release checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a first playable action RPG loop
• Adding combat, enemies, and rewards in stages
• Organizing equipment, quests, and zone data

Search the web and specialized subject areas, run multiple searches in parallel, and extract page text from URLs. Use the bundled command-line tools to gather current information, check claims, and read beyond search snippets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Researching current news and documentation
• Checking claims against multiple sources
• Searching specialized finance or academic sources

Compare Google and Meta advertising results with industry benchmarks suited to your campaign type, budget, and account context. Identify strengths, likely causes of weak performance, and improvement opportunities ranked by potential impact.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding improvement opportunities for a prospective client
• Adding context to quarterly or annual performance reviews
• Assessing whether customer acquisition costs are reasonable

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate beautiful interactive timeline HTML pages from JSON data, with vertical, horizontal, or dual-side layouts, collapsible details, and custom colors. Ideal for project milestones, company histories, or resumes. Triggered when a user mentions 'timeline', 'history of events', 'project milestones', 'release log', or asks to visualize a chronological sequence of data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Break a plan or spec into vertical slices that each declare what blocks them — use when work is agreed but not yet cut into fileable pieces

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when writing, reviewing, or refactoring SwiftUI code for iOS or macOS, including state and `@Observable` data flow, view composition, resizable layouts, safe areas, display scale, performance, lists, environment, localization, animation, Liquid Glass, and API migration. Also use for iPhone Duo, foldable, or large-display layouts (`NavigationSplitView` on large displays, iPhone tab sidebar, two-column reflow, foldable grids, `ArrangementView`, `ReservedRegion`), hinge effects, vertical bars, `@State` initialization or synthesized-property diagnostics, `@ContentBuilder` ambiguity, `reorderable` drag/drop, custom `AsyncImage` `URLSession`, swipe actions outside List, item-bound `alert`/`confirmationDialog`, `ToolbarOverflowMenu`, `AnimatableValues`, Document APIs (`Document`/`DocumentReader`), and Instruments `.trace` capture or analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plans the stack, database schema and API for an app clone, from the recon map replica-recon wrote. Picks boring, managed tech, turns the inferred data model into real SQL with indexes and access rules, lists every route by flow, and orders the build as a thin vertical slice first. Use when the user says "plan the clone", "what stack should I use", "design the database", "write the schema", "plan the API", "architecture for my version of X", or after /replica-recon finishes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Rebuilds an app screen by screen from the recon map: app shell first, then the core flow as a vertical slice, then every screen with all its states, ticking off the feature matrix as it goes. Writes every line fresh, never the original's code, assets or copy. Use when the user says "build the clone", "build screen S07", "start building", "rebuild this screen", "implement the booking page", or after /replica-design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Render an interactive options payoff chart — expiry P&L plus Black-Scholes theoretical value — with sliders for strikes, premium, IV, DTE, and spot. Use this skill whenever the user describes or shares an options position or strategy and wants to see how it makes or loses money: vertical, calendar, diagonal, or ratio spreads, butterflies, condors and iron condors, straddles, strangles, covered calls, protective or naked puts, or any multi-leg structure. This includes pasted strikes, premiums, and expiries, requests like "show me the payoff" or "draw the P&L curve", and broker screenshots (IBKR, TastyTrade, Robinhood). Use it even when details are partial — defaults fill the gaps.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

The reference presentation-tool contract, covering server-side enrichment, suggestion chips, the event stream, progressive rendering, both roles' built-in components, and adding a vertical component. Load when building or reviewing how a commerce agent's output reaches a frontend.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters. Covers Day-0 checklist, SKU selection (Automatic vs Standard), networking options (private API server, Azure CNI Overlay, egress configuration), security, and operations (autoscaling, upgrade strategy, cost analysis). WHEN: create AKS environment, provision AKS, enable AKS observability, design AKS networking, choose AKS SKU, secure AKS, optimize AKS, AKS spot nodes, AKS cluster-autoscaler, rightsize AKS pod, pod rightsizing, over-provisioned AKS pod, pod resource requests and limits, Vertical Pod Autoscaler, VPA recommendations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user asks to "build a media list for my launch", "write a launch press release", or "pitch press under embargo"; produces a three-tier media and analyst list (Tier 1 exclusive candidates, Tier 2 vertical press, Tier 3 communities and newsletters), an embargo pitch timing skeleton keyed to the launch-registry date, a press-release draft in standard structure with no fabricated quotes or numbers, and an analyst briefing outline. Not for press-kit assets — use launch-asset-packager; not for follow-up sequence mechanics — use outreach-manager; not for post-launch news-echo monitoring — use launch-monitor. 媒体名单/禁运期pitch/新闻稿/分析师简报

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Decides whether and how to enter a new market — sizing demand from the bottom up rather than from a market report, testing whether your advantage transfers, choosing between organic entry, partnership and acquisition, sequencing the operational and regulatory work that entry actually requires, and setting the criteria that would tell you to stop. Use this to evaluate a new geography, segment or vertical, pressure-test an entry plan, or work out why a launched market never reached scale.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Edit video and audio with local FFmpeg from natural-language requests: cut, trim, join, resize/reframe (9:16, 1:1), speed change, captions and subtitles (SRT/ASS, animated, karaoke), logos and text overlays, lower-thirds and titles, silence removal, multicam and external-mic sync, loudness normalisation, HDR/Dolby Vision to SDR, LUTs, background music with ducking, platform exports (YouTube, Reels, TikTok, X), compliance checks, scene detection and highlight reels, contact sheets to inspect results, and whole-edit project files. Use this skill whenever the user mentions a video or audio file (mp4, mov, mkv, wav, m4a), footage, a clip, captions, subtitles, a reel or short, YouTube/Instagram/TikTok delivery, LUFS, sync, transcoding, ffmpeg, or asks to make something "60 seconds", "vertical", "louder", "captioned" — even when they do not say "edit". Python 3.9 standard library only, no cloud, no API keys.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build a playable prototype in about an hour to answer one question — is it fun? — with greybox primitives, a hard timebox, and explicit keep/kill criteria. Use when prototyping a mechanic, making a vertical slice or MVP, greyboxing/blockout, or judging throwaway vs keep.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters. Covers Day-0 checklist, SKU selection (Automatic vs Standard), networking options (private API server, Azure CNI Overlay, egress configuration), security, and operations (autoscaling, upgrade strategy, cost analysis). WHEN: create AKS environment, provision AKS, enable AKS observability, design AKS networking, choose AKS SKU, secure AKS, optimize AKS, AKS spot nodes, AKS cluster-autoscaler, rightsize AKS pod, pod rightsizing, over-provisioned AKS pod, pod resource requests and limits, Vertical Pod Autoscaler, VPA recommendations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Help users decide where to apply AI effectively, manage the transition from deterministic to probabilistic software, and build long-term defensibility through verticalization and proprietary data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical (restaurant, healthcare, legal, home services, real estate, automotive). Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "local rankings", "service area", "multi-location", or "local search".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical. Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "service area", or "multi-location".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect missing authentication and broken function-level authorization vulnerabilities in a codebase using a three-phase approach: recon (map endpoints and the role/permission system), batched verify (check auth/authz in parallel subagents, 3 endpoints each), and merge (consolidate batch results). Covers unauthenticated access and vertical privilege escalation (e.g., regular user accessing admin-only functions). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/missingauth-results.md. Use when asked to find missing auth, broken access control, or privilege escalation bugs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.