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Fix descriptions to adhere to character limits
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name: docx
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description: "Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks"
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description: "Document toolkit (.docx). Create/edit documents, tracked changes, comments, formatting preservation, text extraction, for professional document processing."
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license: Proprietary. LICENSE.txt has complete terms
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name: pdf
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description: "Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale."
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description: "PDF manipulation toolkit. Extract text/tables, create PDFs, merge/split, fill forms, for programmatic document processing and analysis."
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license: Proprietary. LICENSE.txt has complete terms
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name: pptx
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description: "Presentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or editing content, (3) Working with layouts, (4) Adding comments or speaker notes, or any other presentation tasks"
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description: "Presentation toolkit (.pptx). Create/edit slides, layouts, content, speaker notes, comments, for programmatic presentation creation and modification."
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license: Proprietary. LICENSE.txt has complete terms
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name: xlsx
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description: "Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas"
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description: "Spreadsheet toolkit (.xlsx/.csv). Create/edit with formulas/formatting, analyze data, visualization, recalculate formulas, for spreadsheet processing and analysis."
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license: Proprietary. LICENSE.txt has complete terms
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name: exploratory-data-analysis
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description: "Comprehensive exploratory data analysis (EDA) toolkit for analyzing datasets and generating actionable insights. Use this skill when users provide data files and request analysis, exploration, insights, or understanding of their data. Handles CSV, Excel (.xlsx/.xls), JSON, Parquet, TSV, Feather, HDF5, and Pickle files. Automatically performs statistical analysis including distributions, correlations, outlier detection, missing data patterns, and data quality assessment. Generates professional visualizations (histograms, box plots, correlation heatmaps, scatter matrices) and comprehensive markdown reports with automated insights. Key triggers: \"analyze this data\", \"explore this dataset\", \"what's in this file\", \"data insights\", \"statistical summary\", \"data visualization\", \"EDA\", \"exploratory analysis\", \"data profiling\", \"understand my data\", \"find patterns\", \"data quality\", \"missing data\", \"outliers\", \"correlations\", \"distributions\". Always outputs structured markdown reports with embedded visualizations and actionable recommendations."
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description: "EDA toolkit. Analyze CSV/Excel/JSON/Parquet files, statistical summaries, distributions, correlations, outliers, missing data, visualizations, markdown reports, for data profiling and insights."
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# Exploratory Data Analysis
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name: hypothesis-generation
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description: "Generate robust, testable scientific hypotheses grounded in existing literature. Use this skill when users need to formulate hypotheses from observations, design experiments to test hypotheses, explore competing explanations for phenomena, develop testable predictions, or create mechanistic explanations across any scientific domain. This skill is essential for hypothesis formation, experimental design, developing testable predictions, proposing mechanistic explanations, generating alternative theories, designing studies to distinguish between competing hypotheses, creating falsifiable predictions, and systematically evaluating hypothesis quality. Apply when users ask about \"why\" something happens, need to explain observations, want to test theories, design experiments, propose mechanisms, generate predictions, or explore alternative explanations in biology, chemistry, physics, medicine, psychology, or any scientific field."
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description: "Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains."
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# Scientific Hypothesis Generation
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name: peer-review
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description: "Comprehensive scientific peer review toolkit for evaluating manuscripts, papers, preprints, and research documents across all disciplines. Use this skill to conduct systematic peer review following established scientific standards, providing constructive feedback on methodology, statistical analysis, experimental design, data interpretation, reproducibility, ethical considerations, and scientific rigor. Includes structured evaluation workflows, reporting standards compliance checks, figure/data integrity assessment, and guidance for writing professional review reports. Applicable to original research articles, reviews, meta-analyses, methods papers, short reports, and preprints in biology, chemistry, physics, medicine, computational sciences, and interdisciplinary research. Essential for manuscript evaluation, grant review, conference paper assessment, and maintaining scientific quality standards."
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description: "Systematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and grant review across disciplines."
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# Scientific Critical Evaluation and Peer Review
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name: scientific-brainstorming
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description: "Structured conversational brainstorming partner for scientific research ideation and creative problem-solving. Activates when scientists need to: generate novel research ideas and hypotheses; explore interdisciplinary connections and cross-domain analogies; challenge research assumptions and conventional thinking; overcome creative blocks and mental barriers; develop innovative methodologies and experimental approaches; synthesize disparate concepts into coherent research directions; identify unexpected research opportunities and unexplored angles; brainstorm solutions to complex scientific problems; expand research scope beyond obvious approaches; connect findings across different scientific fields; develop collaborative research proposals; explore \"what if\" scenarios and alternative hypotheses; identify gaps in current scientific understanding; generate research questions from preliminary observations; develop creative approaches to experimental design; brainstorm applications of emerging technologies; explore unconventional data analysis methods; identify novel research collaborations; develop scientific communication strategies; and think through research problems from multiple fresh perspectives. This skill provides structured brainstorming workflows including divergent exploration, connection-making, critical evaluation, and synthesis phases, while maintaining conversational collaboration and domain-aware guidance across scientific disciplines."
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description: "Research ideation partner. Generate hypotheses, explore interdisciplinary connections, challenge assumptions, develop methodologies, identify research gaps, for creative scientific problem-solving."
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# Scientific Brainstorming
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name: scientific-critical-thinking
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description: "Apply systematic scientific critical thinking to rigorously evaluate research methodology, statistical analyses, evidence quality, and scientific claims. Use this skill when: analyzing research papers for methodological flaws and biases; evaluating experimental designs for validity threats; assessing statistical methods, power, multiple comparisons, and effect sizes; identifying logical fallacies and cognitive biases in scientific arguments; reviewing evidence hierarchies and GRADE criteria; critiquing causal claims vs correlational findings; evaluating study quality using established frameworks (Cochrane ROB, Newcastle-Ottawa); detecting publication bias, p-hacking, and selective reporting; assessing confounding, selection bias, and measurement validity; reviewing research proposals and study protocols; evaluating media reports of scientific findings; conducting systematic literature reviews; determining confidence levels in scientific conclusions; distinguishing between exploratory and confirmatory findings; and providing constructive methodological feedback for improving research rigor."
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description: "Evaluate research rigor. Assess methodology, experimental design, statistical validity, biases, confounding, evidence quality (GRADE, Cochrane ROB), for critical analysis of scientific claims."
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# Scientific Critical Thinking
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name: scientific-visualization
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description: "Create publication-ready scientific figures, plots, charts, and visualizations using matplotlib, seaborn, and plotly. Use this skill for any scientific data visualization task including: creating figures for research papers and manuscripts; preparing plots for journal submission (Nature, Science, Cell, PLOS, PNAS, etc.); making publication-quality figures with proper resolution, fonts, and formatting; ensuring colorblind accessibility and accessibility compliance; creating multi-panel figures with consistent styling; visualizing statistical data with error bars, significance markers, and proper statistical representation; exporting figures in correct formats (PDF, EPS, TIFF, PNG) with appropriate DPI; following journal-specific requirements and style guidelines; improving existing figures to meet publication standards; creating figures that work in both color and grayscale; visualizing experimental results, data analysis outputs, statistical comparisons, time series, distributions, correlations, heatmaps, scatter plots, bar charts, line plots, box plots, violin plots, and other scientific plot types; ensuring figures are clear, accurate, accessible, and professional; applying proper typography, color palettes, and layout principles; creating figures for presentations, posters, and scientific communication; visualizing genomics data, microscopy images, experimental measurements, and research findings."
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description: "Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots."
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# Scientific Visualization
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name: scientific-writing
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description: Comprehensive toolkit for writing, structuring, and formatting scientific research papers, manuscripts, and academic documents. This skill should be used when drafting or revising scientific manuscripts, structuring research papers using IMRAD format, formatting citations and references, creating effective figures and tables, applying reporting guidelines (CONSORT, STROBE, PRISMA), writing abstracts or specific paper sections, adhering to journal submission requirements, ensuring proper use of field-specific terminology and nomenclature, or improving scientific writing clarity and precision. Supports multiple citation styles (APA, AMA, Vancouver, Chicago), provides field-specific reporting standards and linguistic conventions, and ensures compliance with academic writing conventions across biomedical, life sciences, engineering, physical sciences, neuroscience, ecology, and social sciences disciplines.
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description: "Write scientific manuscripts. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), abstracts, for research papers and journal submissions."
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# Scientific Writing
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name: statistical-analysis
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description: "Comprehensive statistical analysis toolkit for rigorous academic research using Python. This skill handles hypothesis testing (t-tests, ANOVA, chi-square, non-parametric tests), regression analysis (linear, multiple, logistic), correlation analysis, Bayesian statistics, and power analysis. It provides systematic workflows for test selection, assumption checking, effect size calculation, diagnostic visualization, and APA-style reporting. Use this skill when you need to: analyze data statistically, choose appropriate statistical tests, check assumptions before analysis, calculate effect sizes and confidence intervals, conduct power analysis for study planning, perform hypothesis testing or regression analysis, interpret statistical results, create publication-ready statistical reports, handle assumption violations, conduct Bayesian analysis, or generate diagnostic plots and statistical visualizations. Essential for research data analysis, experimental design validation, statistical modeling, and academic reporting."
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description: "Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research."
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# Statistical Analysis
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