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House rules and craft for LaTeX Beamer slide decks (lectures, seminars, talks), drawn from the author's own Overleaf edits and published slide-design guidance. Covers storyboarding, frame titles, slide prose, word and frame budgets, layout without overfull boxes, XeLaTeX fonts and Korean (xeCJK), color, figures and screenshots, Overleaf source hygiene, speaker notes, and a build-and-QA loop with a contact sheet. Use when writing, editing, reviewing, or building a Beamer deck, theme, or storyboard. A course skill supplies course facts and the frame skeleton; this skill supplies the craft. Not for HTML or PowerPoint decks, posters, or papers.

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Router for the /docs command. Look up Claude documentation, ask plain-English questions, generate interactive courses and HTML changelog reports, check freshness, and sync the local cache. Dispatches to the claude-docs, claude-docs-validate, claude-docs-course, and claude-docs-changelog skills.

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将学术论文转换为交互式 HTML 课程 + Markdown 文档 + PPTX 演示文稿，一键生成全部三种输出

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"Habitual Domain (HD) theory by Taiwanese scholar P.L. Yu (游伯龍, 1977) — integrates four domains (potential/actual/reachable/activated), eight general hypotheses, seven-layer decision structure, and nine HD-expansion tools to analyze decision blind spots, break cognitive inertia, and support cross-domain integration. Use for diagnosing why alternatives are invisible, decision paralysis, breaking cognitive frames, or explaining perception gaps between people. Triggers: 『思維框架』『打破盲點』『創新思考』『決策盲區』『跨域整合』『游伯龍』『習慣領域』『HD 理論』『七層決策』『四領域』『八大通性』『P.L. Yu』. Especially for NYCU EMBA HD course, 政大科管所, 中山企管系 decision-analysis/innovation/leadership courses. Taiwan-origin theory cited in J. Optimization Theory, Theory & Decision, Human Systems Management.".

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Use when a task needs the judgment of an Engineering Teacher, Postsecondary — diagnosing a spike in a gatekeeper course's D/F/W rate, running a capstone design course with an industry sponsor, closing the loop on an ABET student-outcome assessment, or building a tenure/promotion teaching-effectiveness case beyond raw student evaluation scores.

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Use when a task needs the judgment of an Area, Ethnic, or Cultural Studies Professor — designing a joint-appointment course load across two departments, prepping a tenure or mid-tenure review file for interdisciplinary scholarship, deciding whether a course survives an enrollment or political-pressure threat, evaluating community-engaged research for a merit case, or writing a Title VI/FLAS-adjacent grant narrative.

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Learn effectively from courses and academies: choosing programs, studying actively, and converting courses into skills. Use when investing time/money in structured learning.

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Create interactive AI role-play training activities as self-contained HTML. Use when the user asks to generate a role-play, persona practice, sales simulation, customer conversation drill, compliance scenario, or any learner-vs-AI-character training exercise.

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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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Manage significant changes during sprint execution. Use when the user says "correct course" or "propose sprint change"

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[Workflow] Use when activating the Research & Synthesis workflow — research a topic via web sources, then synthesize per --output={synthesis|business-eval|marketing|course} (knowledge report, business evaluation, marketing strategy, or course material).

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[Content] Use when you need to build structured learning/teaching course material with Bloom taxonomy objectives, modules, lessons, exercises, and assessments.

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Create a study cheatsheet by extracting and synthesizing key concepts from lecture materials, homework, and past exams. Use this skill whenever a user asks for help making a cheatsheet, study sheet, formula sheet, exam reference sheet, or "one-pager" — especially when they upload lecture slides (PDF/PPTX/DOCX), problem sets, past exams, or scanned/handwritten notes. Trigger this even for casual phrasings like "help me prep for my final," "summarize my course materials into something I can bring to the exam," or "make me a study reference from these slides and homeworks." Tuned for math/science (formulas, proofs, theorems) and CS/engineering (code, algorithms). Processes one file at a time and persists intermediate summaries to disk to avoid blowing past the context window on large courses.

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Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

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Expert en produits digitaux avancé (ebooks, templates, presets, Notion, courses)

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Expert en création de cours avancé (curriculum, video, LMS, assessments, community, monetization)

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Community monetization — Discord monetization, courses, memberships, premium tiers

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End-to-end bulk time-course analysis from an expression matrix to temporal gene modules and per-cluster pathway enrichment. Orchestrates temporal DE (limma splines or DESeq2 LRT), Mfuzz/tslearn soft clustering of expression-profile shapes, GAM trajectory fitting, per-cluster GO enrichment against a temporal-gene background, and an OPTIONAL circadian rhythm-detection branch (MetaCycle/CosinorPy) that runs only when the design covers >=2 full cycles with >=6-8 evenly spaced samples per cycle. Use when analyzing a bulk time-series expression experiment from any omics platform and deciding limma-splines vs DESeq2-LRT for temporal DE, soft vs hard clustering, whether the sampling design even licenses rhythm detection, and which background to use for enrichment. Not for single-cell pseudotime (see temporal-genomics/trajectory-modeling for the bulk-vs-pseudotime boundary) or unknown-period discovery (see temporal-genomics/periodicity-detection).

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有馬記念分析システムの命名ドメイン適合性レビュー。関数名・変数名・型名が競馬ドメインの意図（馬・騎手・調教師・血統・レース・出走・着順・上がり3F・馬場・会場・枠順・ローテ）を表現しているか、汎用名・技術接尾辞・命名規則の逸脱（snake_case 混入・アンダースコア付きメソッド名）・同一概念に別名が混在していないか（venue と course、同名異義の型）を検証。Use when user says '命名レビュー', 'review naming', '名前チェック', or 'ドメイン命名確認'.

Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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。

Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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Use this skill to understand defensive driving course expectations for CMV drivers. Covers federal vs state vs carrier-specific requirements.

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Transform academic course assignment PDFs into structured, actionable markdown checklists with dates, unique IDs, and custom tags. Asks when the user will start, assigns tasks only to weekdays (Monday-Friday), respects weekends automatically, and calculates forum deadlines 3 days before due date. Use this skill when the user uploads academic PDFs or asks to create a task plan from course assignments.

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Canvas LMS integration — fetch enrolled courses and assignments using API token authentication.

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。