Network-science primitives: centrality, influence seeding, communities, contagion vs homophily, GNN failures. Use when seeding a referral campaign or finding critical graph nodes.
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。
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Network-science primitives: centrality, influence seeding, communities, contagion vs homophily, GNN failures. Use when seeding a referral campaign or finding critical graph nodes.
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。
Builds ML pipelines: EDA, features, leakage checks, evaluation. Use when doing data science or explaining fairness, privacy, speech, vision-language, or diffusion mechanics.
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。
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' 'consumer behavior,' 'anchoring,' 'social proof,' 'scarcity,' 'loss aversion,' 'framing,' or 'nudge.' Use this whenever someone wants to understand or leverage how people think and make decisions in a marketing context. For applying psychology to specific pages, see cro; for pricing tactics, see pricing; for copy framing, see copywriting.
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。
Foundational concepts in nutrition science — macronutrients, micronutrients, energy balance, Atwater factors, reference intakes (DRI/RDA/AI/UL), food composition tables, and the methods of human nutrition research. Grounds the rest of the department in a shared vocabulary and in the measurement limits of the field, including controlled-trial history, observational-study limits, and the biochemical basis for calorie accounting. Use when a question asks what something "is" nutritionally, how energy and nutrients are measured, or what the reference numbers mean.
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。
Climate physics, forcing, feedback, and attribution. Covers the greenhouse effect and radiative forcing, climate sensitivity, fast and slow feedbacks, the paleoclimate record, detection and attribution science, regional climate impacts, and the distinction between weather, climate variability, and forced climate change. Use when reasoning about global warming mechanisms, interpreting temperature or CO2 records, evaluating attribution claims for extreme events, or distinguishing signal from natural variability.
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。
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.
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。
Designs publication figures for social-science manuscripts. Covers chart choice, axes and scales, color and accessibility, legend order, captions that carry the title, and script-based reproducibility. Use when making, revising, or critiquing a figure for a paper, such as a coefficient plot, small multiples, a distribution, a trend, a map, or a network. Finished figure sets go to figure-table-audit for end-stage QA.
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。
Designs publication tables for social-science manuscripts. Covers whether a result belongs in a table at all, column order, row grouping and panels, decimal precision, standard-error and confidence-interval conventions, self-contained titles and notes, and code-generated output. Use when building or revising a regression, balance, descriptive, or robustness table, or when working with modelsummary, stargazer, gt, esttab, or booktabs. Finished tables go to figure-table-audit for end-stage QA.
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。
Scaffolds or audits a social-science replication package and evaluates the manuscript and archived research objects against FAIR principles. Scaffold mode writes the folder structure, README, master.R, figure-table crosswalk, codebook, LICENSE placeholder, and .gitignore; audit mode grades the package against a pre-release checklist and audits data, code, materials, identifiers, licenses, access restrictions, and availability statements. Use when setting up or repairing a package, checking one before submission, auditing FAIR compliance, or drafting availability statements. Adapted from Yusaku Horiuchi’s replication-package-guide.
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。
Audits a methods section against a 45-item reporting checklist synthesized from the APSA Experimental Section rubric, JARS-Quant, CONSORT, and DA-RT, covering pre-registration, recruitment, randomization and treatments, sample flow and attrition, sample-size justification, three-tier results labeling, conjoint reporting, validity, and open-science infrastructure. Use when checking whether a methods section reports enough, preparing a submission or replication archive, asking what CONSORT, JARS, or DA-RT require, or documenting a deviation from a pre-analysis plan. Writing that plan before data collection goes to pre-registration-writing.
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。
Scaffold or audit a social-science replication package, and audit the manuscript and archived research objects against FAIR. Use when setting up or repairing a package, checking one before submission, or drafting data, code, and materials availability statements. Adapted from Yusaku Horiuchi's replication-package-guide.
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。
Nature-style materials science manuscript reviewer skill for testing materials design, synthesis, processing, structure-property, mechanism, stability, scalability, computational prediction and application claims against materials evidence chains.
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。
Nature-style atmospheric science manuscript reviewer skill for testing observation, reanalysis, model, aerosol-cloud-radiation, extreme-event, circulation-mechanism and attribution claims against their physical evidence chain.
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。
Score a PhD applicant's profile and rank candidate advisors using a connection-first 4.0-scale scoring system. Best-supported for physics / HEP and materials science (MSE), with the scoring engine extensible to chemistry, biology, CS, math, EE, ChemE, earth science (each with field-specific caveats — see references/journal_tiers.md). Use when the user wants to evaluate their PhD application chances, find matching advisors at top US programs, score a CV for graduate school, or compare candidate professors. Also triggers when the user mentions phdtaketaketake or its connection-first philosophy of valuing advisor network over h-index.
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Use this skill when the user wants to download paper PDFs by DOI(s). Triggers: "下载这个/这几个 DOI的 PDF/全文", "把这个 DOI 列表全部下下来", "批量下 dois.txt", "download paper(s) for DOI(s) …", "bulk-download from this Web of Science / WoS savedrecs export". Routes each DOI through publisher TDM APIs (Wiley/Elsevier/Springer) then OA fallbacks (OpenAlex/Crossref/Unpaywall), and — when --use-browser-fallback is set — a final Playwright pass that reuses institutional cookies for ACS/RSC/IEEE/AIP/IOP/APS/Science. DO NOT use for paper *search* (use Semantic Scholar / arXiv instead), Zotero import / metadata sync, or downloading *all references of* a single paper (use the ref-downloader skill).
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。
Writes expert academic paper summaries for social science research, particularly political science and applied statistics. Use when asked to summarize, review, or create a reading summary of an academic paper, PDF, or research article. Accepts a file (PDF or text format) or a directory of papers. Produces a structured markdown summary — approximately 400–600 words — covering primary contributions, major questions and answers with point estimates, methods and data, and limitations and robustness. Includes BibTeX citation retrieved from Google Scholar and keyword metadata.
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 the user asks to track, scan, monitor, digest, or maintain a reading queue for new papers from top journals, Nature/Science/Cell/PNAS families, or any research field. Works with any discipline — ecology, environment, CS, medicine, physics, social sciences, etc.
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 task needs the judgment of an Environmental Science Professor — deciding whether a field exercise crosses a weather/safety cancel threshold, building an NSF/EPA proposal budget, normalizing an interdisciplinary tenure case against the right benchmark, checking whether a mixed-methods field project needs IRB review, or handling a politically charged classroom topic without losing academic-freedom footing.
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Use when a task needs the judgment of a Postsecondary Computer Science Professor — diagnosing why a course's DFW (D/F/withdraw) rate spiked, redesigning an intro programming course's assessment structure, staffing TAs and grading load for a large course, deciding what to prioritize pre-tenure, running an academic-integrity (MOSS similarity) investigation, or mapping a curriculum change against ABET student outcomes.
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 task needs the judgment of a tenure-track or tenured Agricultural Sciences faculty member at a land-grant university — deciding whether to submit a grant this cycle versus wait, allocating time across the teaching/research/extension appointment split, diagnosing a stalled graduate student or field trial, or drafting a tenure-and-promotion dossier narrative.
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。
SciPilot Skills 家族成员,负责科研数据可视化——但定位不是"画图工具", 而是"可视化顾问"。先做数据剖析(列类型/样本量/分布/异常值/分组结构/相关性), 再结合用户的论证目标推荐图型,主动拦截科研画图的经典错误(小样本画均值柱掩盖 分布、双 Y 轴、饼图、Y 轴不当截断、rainbow 色图、把分类点连成折线等),最后 产出 Nature / Science / IEEE / Elsevier / PNAS / 中文核心期刊级别的成图。 覆盖纯数据图:折线、柱状、散点、箱线 / 小提琴、热力图、误差棒、分布图(直方 图 / KDE)、相关性矩阵 / 散点矩阵、多面板组合。技术栈 matplotlib + seaborn + SciencePlots(静态)+ plotly(交互)。中英文双语,中文模式自动配置 Noto Sans CJK / Source Han Sans / SimHei 并修复负号方框,支持中文期刊"宋体正文 + Times New Roman 数字"混排。默认色盲安全配色 + 冗余编码 + 灰度预览。出图后做 "视觉自检闭环":渲染 PNG 预览→程序自检缺字/文字裁切/刻度重叠→AI 读图复核遮盖 与子图对齐→回改重渲,直到通过。 当用户的任务涉及以下任何情况时主动触发:论文配图、科研画图、数据可视化、 不知道用什么图、怎么展示数据、用什么图好、期刊投稿图、figure、出版级图表、 matplotlib、seaborn、plotly、误差棒、显著性标注、色盲安全配色、矢量图导出、 中文论文图表、多面板。**即使用户只是给一批数据问"这个怎么画"或"用什么图 好",也应使用本技能——本技能首要能力是"判断该用什么图",其次才是绘制。** 不做示意图、流程图、架构图。
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。
Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.
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。
Climate science expert explaining climate systems, climate change, models, and environmental impacts
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。
Neuroscience expert covering brain structure, neural signaling, cognition, and neurological disorders
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。