Results for “course”

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'.

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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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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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Analyze repeated non-engagement, deflection, motte-and-bailey shifts, tone policing, sealioning, and related manipulative discourse patterns. Use when the user suspects they are being rhetorically managed rather than answered, or when they need a careful pattern read grounded in quotes and alternatives. NOT for psychiatric diagnosis, legal judgment, or help deploying bad-faith tactics.

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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.

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Scaffold course exercise directories with numbered sections, exercise variants, required readmes, and repo-specific validation. Use when creating exercise stubs, adding a course section, or safely renaming exercises.

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Full exploit development course roadmap and syllabus: weekly topics, recommended reading, lab setup, and learning path from vulnerability classes through advanced exploitation. Use to structure exploit dev training or onboard new researchers.

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Research recent X/Twitter reactions to markets, companies, products, or communities. Produce a reusable Markdown briefing that groups supportive, critical, and neutral views, with citations, coverage details, and an assessment of overall sentiment.

• Checking reactions to a product on X/Twitter
• Comparing positive and negative views of a company or stock
• Reviewing social reactions to a market event

Create an employee training deck about recognizing phishing and responding in the first 60 seconds. The HTML slides use a Windows 95-inspired design, with editable content and an adjustable slide count that preserves the template’s visual style.

• Preparing employee training on phishing awareness
• Explaining what to do in the first 60 seconds
• Creating security slides for employee onboarding

Create a retail training deck about consultative selling, covering the sales conversation, role plays, and daily practice. The HTML slides use a warm peach palette and consistent, approachable layouts for store associates and floor managers.

• Prepare consultative selling training for store associates
• Create onboarding slides about the sales conversation
• Build a workshop deck with retail role plays

Create an HTML workshop deck for new users of a project management app. Organize training around first useful results and ongoing habits, using a warm pastel template with hand-drawn decorations.

• Preparing onboarding workshops for new customers
• Explaining the path to a first useful result
• Teaching habits for continued app use

Create investment-banking-style presentations for board discussions and M&A decisions. Organize comparable companies, past transactions, valuation ranges, and strategic options into slides that distinguish sourced figures from assumptions and explain the recommended course of action.

• Preparing board materials on strategic alternatives
• Comparing peer companies and past M&A transactions
• Presenting valuation ranges and sensitivity analysis

Create static SVG diagrams, scalable images for lessons, READMEs, and manuals. It uses real system names, nine diagram types, and consistent colors and arrows, with checks for readable labels, accessible descriptions, and valid markup.

• Add a diagram of system interactions to a README
• Explain state transitions in a manual
• Illustrate data flow from logs or specifications

Create animated lesson diagrams, sliders that update figures, and interactive panels that explain how systems work. It also builds moving dot patterns for page headers, with rules for readability, themes, and reduced motion.

• Animate a process in a lesson
• Explore a formula with a slider
• Show a system's behavior through one control

Study AI engineering through interactive terminal lessons with explanations, coding exercises, and quizzes. The tutor follows your recorded progress, revisits previous material, adapts to your answers, and logs results and topics that need review.

• Resuming lessons from recorded progress
• Learning concepts through coding exercises
• Checking understanding with lesson quizzes

Start AI Engineering from Scratch with a brief interview and placement quiz. Create a persistent LEARNING.md study plan recording your goals, starting phase, pace, and learning path for later sessions with the learn skill.

• Creating a study plan when starting the curriculum
• Choosing a starting phase based on current knowledge
• Recording learning goals, available time, and a build goal