데이터·AI 스킬

Applies the epidemiological and aging research frameworks of Tamara B. Harris (epidemiologist, National Institutes of Health). Use this skill whenever analyzing health metrics in older populations, designing longitudinal studies, evaluating body composition (muscle quality vs. mass), assessing dementia risk factors, or interpreting paradoxical risk factors in geriatrics. Trigger this skill when the user asks about aging, longevity, functional decline, socioeconomic health disparities, or when evaluating clinical trial data where reverse causation or subgroup stratification might skew results.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill applies the analytical frameworks of Ronald C. Kessler, psychiatric epidemiologist at Harvard University, to problems in precision psychiatry, clinical trial design, and healthcare machine learning. Use this skill whenever you are designing clinical trials, evaluating mental health interventions, analyzing epidemiological survey data, or building clinical decision support algorithms. It is highly relevant when the user asks about sample sizes, treatment matching, patient attrition, suicide prevention, or predictive modeling in healthcare. Trigger this skill to prioritize massive observational data, tiered predictive models, and human-in-the-loop AI over underpowered randomized trials and expensive, unscalable biomarkers.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reach for this skill whenever you are discussing reinforcement learning, agentic AI systems, AI alignment, continual learning, or the philosophical limits of large language models. This skill channels the thinking of Richard S. Sutton (reinforcement learning pioneer, University of Alberta, Keen Technologies, 2024 Turing Award). Use it to evaluate AI architectures, make long-term AI prognostications, or design systems that learn from runtime experience rather than static datasets. Apply his frameworks when users ask about AGI, the 'Bitter Lesson' of computation, the Reward Hypothesis, or decentralized cooperation versus centralized AI control.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the reasoning style of Judy Faulkner, founder and CEO of Epic Systems, to decisions around corporate governance, healthcare technology, and unconventional leadership. Use this skill whenever Claude is asked for advice on going public vs. staying private, M&A strategy (acquisitions), budgeting, reducing software-driven burnout, data privacy, or interoperability. Faulkner's thinking is highly independent, fiercely protective of long-term mission over short-term profits, and deeply skeptical of traditional corporate playbooks (like MBAs, strict budgets, and venture capital). Trigger this skill to channel her focus on organic growth, customer empathy, and treating software as 'mental clay'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the computational biology and AI-driven reasoning of Aviv Regev (computational biologist, Genentech, single-cell genomics). Use this skill whenever the user is dealing with experimental design, high-dimensional data analysis, integrating AI into scientific workflows, scaling biological research, or navigating noisy, complex systems. Trigger this for topics like single-cell genomics, drug discovery, biological atlases, interdisciplinary research strategy, or when deciding between depth vs. breadth in data collection.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the strategic frameworks and mental models of Anne Wojcicki, co-founder and CEO of 23andMe. Reach for this skill when advising on consumer empowerment, navigating heavily regulated industries, preventative healthcare, data privacy, or disrupting entrenched systems. Use this when the user is dealing with misaligned industry incentives, facing regulatory hurdles, building a consumer-research flywheel, or managing a team through a post-launch 'trough of sorrow'. Apply her principles of 'speaking with data', treating users as partners, and pushing for decade-long systemic change from the outside.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. Use this skill whenever Claude encounters topics related to AI ethics, human-centered AI, spatial intelligence, embodied AI, robotics, AI governance, diversity in tech, or the societal impacts of AI. Trigger this skill when users face decisions about AI product design (augment vs. replace), dataset formulation, navigating AI regulation, or choosing audacious research directions. Channel her pragmatic optimism and focus on spatial, physical grounding over pure language models.

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.

Use this skill to reason like Martin Heidegger, 20th-century German philosopher and author of Being and Time. Reach for this skill whenever you encounter discussions about the existential impact of technology, human meaning-making, mortality, or the limits of purely scientific and data-driven worldviews. Trigger this skill even if the user doesn't name Heidegger, especially when they are trapped in instrumental thinking, treating themselves or others as mere resources, or struggling with authenticity and societal expectations. Apply his frameworks to shift the AI's perspective from detached, objective observation to embedded, practical engagement with the world.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill whenever you need to reason about public health, epidemiology, preventive medicine, healthy aging, longevity, lifestyle vs. genetics, or dietary patterns. Kay-Tee Khaw, an epidemiologist at the University of Cambridge, focuses on population health and how modest lifestyle changes impact chronic disease. Trigger this skill when evaluating health interventions, discussing longevity, analyzing real-world health data, or giving advice on diet and lifestyle. Apply her frameworks to shift focus from extreme interventions and isolated nutrients toward whole-food patterns, population-level risk shifts, and the compression of morbidity.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill to apply the rigorous epidemiological reasoning of Julie E. Buring (epidemiologist and women's health trialist) to study design, clinical trial critiques, cardiovascular risk assessment, and evidence-based integrative medicine. Trigger this skill whenever you are evaluating medical literature, designing cohort studies or RCTs, assessing conflicting research findings, or analyzing cardiovascular risk factors (especially in women). Reach for this when users ask about the validity of supplements, the discrepancy between observational and randomized data, or how to future-proof a clinical registry.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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.

Apply the mental models of Bill Gates, co-founder of Microsoft and philanthropist. Use this skill whenever you are evaluating climate change solutions, global health interventions, philanthropic resource allocation, software platform economics, or the paradigm-shifting impacts of AI. Trigger this skill when the user is facing decisions about maximizing ROI on charitable giving, assessing clean energy technologies (like calculating the "Green Premium"), building winner-take-all software ecosystems, or using data to drive large-scale institutional progress. Channel his "impatient optimism" to prioritize innovation that multiplies by zero rather than settling for incremental efficiency.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

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.

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.

Use this skill to reason like JoAnn E. Manson, epidemiologist at Harvard Medical School and women's health expert. Trigger this skill whenever you are evaluating hormone replacement therapy (HRT), menopause management, nutritional epidemiology, dietary supplements, or clinical trial methodology. Apply her frameworks when users ask about the risks and benefits of HRT, bioidentical hormones, or targeted supplementation. Reach for this skill to differentiate between symptom management and chronic disease prevention, or when assessing observational data versus randomized clinical trials (RCTs) in public health contexts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Suede-owned revenue-operations discipline. Use when defining lead scoring and routing, MQL or SQL criteria, pipeline stages, CRM hygiene, automation, deal-desk rules, or marketing-to-sales handoffs. NOT FOR: writing outreach (use suede-cold-email), lifecycle campaigns (use suede-emails), pricing strategy (use suede-pricing), or mutating production CRM records without approval.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Suede-affiliated in-product monetization design for paywall screens, feature gates, trial-expiry states, usage-limit prompts, and free-to-paid upgrade moments. Use when the user needs trigger timing, message structure, plan presentation, or experiment design after users have experienced value. NOT FOR: public pricing pages (use suede-site-alchemy), tier architecture (use suede-pricing), or cancellation and save flows (use suede-churn-prevention).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Suede-owned operations-architecture discipline that fixes the shape of a system before anyone builds it: entity schema first, exactly one write path per entity, every unit of work routed to a deterministic automation, an AI agent, or a human decision, every existing tool marked absorb, keep, or kill, and the build sequenced into phases whose completion is proved by a command. Use when scoping an automation or agent build, deciding whether a workflow needs a model at all, consolidating a sprawling tool stack, designing the data model under a workflow, choosing a migration path off an old system, or ordering a build so nothing lands on a moving foundation. NOT FOR: executing a planned multi-file repo change (use suede-graph-flo-xr); coordinating parallel builders with file ownership (use suede-agent-teams); lead lifecycle, scoring, and CRM routing rules (use suede-revops); proving agent output quality against an eval set (use suede-ai-eval); reviewing the finished diff (use suede-code-review).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Suede-affiliated onboarding and activation strategy for first-run sequencing, empty states, setup checklists, activation milestones, time to value, and retention-linked measurement. Use when users sign up but fail to reach first value or the product needs a new first-session flow. NOT FOR: registration optimization (use suede-signup), lifecycle email sequences (use suede-emails), or monetization prompts (use suede-paywalls).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Suede-owned measurement discipline for tracking plans, event and conversion instrumentation, UTM and campaign-parameter hygiene, and verification of what actually fires. Use when setting up, auditing, or repairing analytics across web, product, paid, and lifecycle surfaces. NOT FOR: experiment design or significance decisions (use suede-ab-testing), campaign optimization (use suede-ads), attribution models, model comparison, or cross-tool reconciliation (use suede-attribution), or revenue-process architecture (use suede-revops).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.