877 件 ・ 関連度順
概要と使いどころ
Calculate statistical power and sample sizes for RCTs. Use when user mentions: power analysis, sample size calculation, minimum detectable effect, MDE, statistical power, effect size, clustering effects, design effect, ICC, intra-cluster correlation.
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。
Causal inference with treatment/control groups over time. Use when user mentions: treatment effects, policy evaluation, pre/post comparison, parallel trends, DID, DiD, difference in differences, natural experiment, quasi-experimental.
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。
Summary statistics, data exploration, and descriptive analysis. Use for: summary stats, describe data, data exploration, distributions, means, medians, correlations, cross-tabs.
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。
Generate hand-drawn style diagrams and infographics for recovery education articles. Creates anatomist's notebook aesthetic visuals - brain diagrams, timelines, social comparisons, and process flows using continuous line art, semantic color coding, and margin annotations.
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。
Analytical methods for decomposing expert performance into cognitive and physical task components
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。
Benchmark multi-agent coordination systems with experiment design, power analysis, human rating protocols, bootstrap confidence intervals, and reproducible reporting. Use for salvage latency, recovery fidelity, coordination overhead, and protocol comparison studies. NOT for ML model benchmarking, web-product A/B testing, survey design, or general-purpose data science.
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。
Analyze hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.
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。
Expert in color theory, palette harmony, and perceptual color science for computational photo composition. Specializes in earth-mover distance optimization, warm/cool alternation, diversity-aware palette selection, and hue-based photo sequencing. Activate on "color palette", "color harmony", "warm cool", "earth mover distance", "Wasserstein", "LAB space", "hue sorted", "palette matching". NOT for basic RGB manipulation (use standard image processing), single-photo color grading (use native-app-designer), UI color schemes (use vaporwave-glassomorphic-ui-designer), or color blindness simulation (accessibility specialists).
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 digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.
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。
Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.
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 this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.
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。
Search arXiv preprint repository for papers in physics, mathematics, computer science, quantitative biology, and related fields
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。
Expert-level polymer science covering polymer structure, thermoplastics and thermosets, mechanical behavior, processing methods, and polymer selection for engineering applications.
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。
Expert-level climatology covering climate system components, climate forcing and feedbacks, paleoclimatology, climate modeling, and climate change science.
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。
Expert-level exoplanet science covering detection methods, orbital dynamics, planetary atmospheres, habitability, and the search for biosignatures.
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。
Expert-level marine biology covering ocean ecosystems, marine organisms, coral reef ecology, deep sea biology, fisheries science, ocean chemistry, and marine conservation.
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。
Expert-level graph theory knowledge. Use when working with graph algorithms, network flows, matching, graph coloring, planar graphs, spectral graph theory, random graphs, or graph applications in computer science and networks. Also use when the user mentions 'shortest path', 'minimum spanning tree', 'network flow', 'bipartite matching', 'graph coloring', 'clique', 'independent set', 'vertex cover', 'planarity', 'graph isomorphism', 'adjacency matrix', or 'Laplacian'.
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。
Expert-level microbiology covering bacterial and viral biology, microbial physiology, pathogenesis, antimicrobial resistance, microbiome science, and industrial microbiology.
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。
Orchestrate an end-to-end data analysis or machine learning project from decision framing through reproducible handoff. Use when a request spans multiple lifecycle stages or an ambiguous modeling request must become a decision-ready result; use narrower audit or experiment-design skills for isolated reviews.
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。
Evidence-grounded AI research workflow for turning supplied economics, finance, management, and social-science papers or structured records into versioned PaperReading artifacts. Use when Codex must ingest text, Markdown, or a text-based PDF; separate source-grounded claims from researcher analysis; bind findings to evidence IDs and locators; review extraction candidates; migrate v0.2 PaperRecord JSON; validate or verify a v0.3 PaperPackage; generate research extensions; export reviewable JSON or Markdown; or safely update a compatible 13-field literature workbook.
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。
Comprehensive data science workflow skill for analyzing tabular and time series data. Use when conducting exploratory data analysis, building predictive models, performing feature engineering, or generating data-driven insights. Supports end-to-end analysis from data understanding through model deployment, following industry best practices for regression, classification, time series forecasting, and statistical analysis.
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。
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.
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。
Knowledge base synthesized from the Guide to the Systems Engineering Body of Knowledge (SEBoK) v2.14 by the BKCASE Project. Use for the established systems-engineering canon: SE fundamentals and foundations (systems science, systems thinking, the systems approach, modelling/MBSE), the life cycle (ISO/IEC/IEEE 15288/24748/24774 process, development & agile approaches, model selection/tailoring), technical management, concept definition, architecture/realization (the Vee), V&V, maintenance/sustainment, SE standards, and the application contexts (product, service, enterprise, systems-of-systems, healthcare), plus enabling SE (business/team/individual), related disciplines, quality attributes/specialty engineering, implementation examples, and emerging knowledge. Good for grounding background, vocabulary, and prior-art positions across all eight SEBoK Parts. SCOPE LIMITS: this pack is SEBoK v2.14 only (released 2026-05-18); it synthesizes and signposts rather than reproducing the source — it does not reproduce ISO/IEC/IEEE standard text, is a curated guide (not a complete textbook), and is thin on AI-integration depth (Part 8 is a staging area, not settled practice). LICENCE: content is CC BY-NC-SA 3.0 — non-commercial use only, attribution to the BKCASE Project required, share-alike obligations carry forward.
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。
Project-first Research pipeline with live gates, source/PDF evidence, claim boundaries, compute and publication controls; includes an optional PubMed/arXiv standard-library helper.
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。