데이터·AI 스킬

Analyzes social media campaign performance across platforms with engagement metrics, ROI calculations, and audience insights for data-driven marketing decisions

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Measure what matters with proper event tracking, funnels, cohorts, and metrics. Use when setting up analytics, tracking features, or understanding behavior.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze China macroeconomic data, PBOC monetary policy, fiscal policy, and their impact on A-share and HK stock markets. Track GDP, CPI, PPI, PMI, social financing, and interpret policy signals. Use when user asks about 中国宏观经济, 央行政策, economic data impact on markets, or needs to understand policy implications for investment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create detailed user personas based on research and data. Develop realistic representations of target users to guide product decisions and ensure user-centered design.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Master metrics definition, KPI tracking, dashboarding, A/B testing, and data-driven decision making. Use data to guide product decisions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate UTM-tagged URLs for campaign tracking. Create consistent, organized tracking links for Google Analytics, HubSpot, and other analytics platforms. Supports bulk generation, naming conventions, and campaign documentation. Use when creating tracking links, campaign URLs, or organizing marketing attribution.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Interpret GA4 and GSC data with benchmarks, status indicators, and actionable insights

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill generates professional LinkedIn announcement text for intelligent textbooks by analyzing book metrics, chapter content, and learning resources to create engaging posts with key statistics, hashtags, and links to the published site. Use this skill when you need to create social media announcements about textbook completion or major milestones.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze user conversion funnels, calculate step-by-step conversion rates, create interactive visualizations, and identify optimization opportunities. Use when working with multi-step user journey data, conversion analysis, or when user mentions funnels, conversion rates, or user flow analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Verifies factual claims in documents using web search and official sources, then proposes corrections with user confirmation. Use when the user asks to fact-check, verify information, validate claims, check accuracy, or update outdated information in documents. Supports AI model specs, technical documentation, statistics, and general factual statements.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Product analytics and growth expert. Use when designing event tracking, defining metrics, running A/B tests, or analyzing retention. Covers AARRR framework, funnel analysis, cohort analysis, and experimentation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Art-direct complete illustrated websites and write project-specific image-generation prompts. Use when the user requests Illustrated Gallery, a gallery-inspired visual style, or $illustrated-gallery. Covers visual direction, original artwork, and page-wide composition; not a fixed website clone, standalone image task, or generic dashboard skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design and verify data contracts, schema changes, migrations, rollbacks, backfills, compatibility windows, tenant isolation, and API-to-database field mapping. Use when changing database schema, event payloads, persisted documents, analytics tables, multi-tenant data boundaries, or any user-visible data contract where silent fallback or undeclared fields would be dangerous.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Are We Anti-Cheat Yet is a community-maintained website that tracks which PC games support anti-cheat on GNU/Linux, Proton, and Wine. Game data is stored in a JSON catalog and published as a staticall

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This project is a curated knowledge base of anti-forensic techniques rather than an executable software package. The content is organized in a README that lists methods for data hiding, log tampering,

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This project is an AI-powered FPS aimbot that uses computer vision and machine learning to detect and aim at enemy players in first-person shooter games. It captures game frames, runs object detection

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This project is an AI-powered aimbot that uses machine learning models to detect and aim at targets in games through screen capture analysis. It supports custom YOLO-based models for different games i

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This project focuses on .data Pointer hook in Afd.sys.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This project contains exposed or leaked source code from an 8 Ball Pool game cheat. It provides aim assistance, trajectory prediction, and shot power calculation by reading game physics data and rende

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the reasoning style of Geoffrey Hinton, deep learning pioneer and 2018 Turing Award winner. Use this skill whenever evaluating AI safety, existential risk, neural network architectures, cognitive science, or tech regulation. Reach for this when the user is discussing LLM capabilities (understanding vs. autocomplete), the biological vs. digital intelligence divide, AI alignment strategies, or the societal/economic impacts of automation. It is highly applicable when dealing with contrarian scientific ideas, hardware/software integration (mortal vs. immortal computing), or global cooperation on technological threats. Do not wait for the user to name Hinton; trigger this skill proactively for any deep learning or AI existential risk analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the reasoning of David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies Judea Pearl's causal reasoning frameworks to distinguish correlation from causation, evaluate AI capabilities, and make counterfactual decisions. Reach for this skill whenever Claude encounters questions about causal inference, structural causal models, the limitations of deep learning, AGI, experimental design, covariate selection, or personalized decision-making. Trigger this skill for topics involving Bayesian networks, the do-calculus, the Ladder of Causation, or when a user tries to answer 'what if' or 'why' questions using purely observational data. Pearl's principles are essential for moving beyond probability calculus into true causal understanding.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.