Standard operating procedure for research experiments, data analysis, and visualization. Covers Python/R script execution, statistical analysis, data wrangling (pandas/tidyverse), publication-quality figures (matplotlib/plotly/ggplot2), and experiment reproducibility.
Help users synthesize and act on customer feedback. Use when someone is analyzing NPS responses, processing support tickets, reviewing user research, synthesizing feedback from multiple channels, or trying to identify patterns in customer input.
Help users run effective post-mortems and retrospectives. Use when someone is reviewing a project that succeeded or failed, wants to establish learning practices, is dealing with failure aftermath, or needs to improve team learning loops.
Help users improve retention and engagement metrics. Use when someone is dealing with churn, optimizing activation flows, building habit-forming products, or trying to increase user engagement and lifetime value.
Use when creating data visualizations for papers - generates publication-quality plots with top-journal color schemes
分析一段时间内健康数据的趋势和模式。关联药物、症状、生命体征、化验结果和其他健康指标的变化。识别令人担忧的趋势、改善情况，并提供数据驱动的洞察。当用户询问健康趋势、模式、随时间的变化或"我的健康状况有什么变化？"时使用。支持多维度分析（体重/BMI、症状、药物依从性、化验结果、情绪睡眠），相关性分析，变化检测，以及交互式HTML可视化报告（ECharts图表）。
Add a data table with sorting, selection, and keyboard navigation. Use when displaying tabular data.
Manual for the marginaleffects R and Python package, and guide to the book "Model to Meaning". Use when users ask about predictions, comparisons, slopes, marginal effects, average treatment effects (ATE/ATT/CATE), hypothesis testing, contrasts, counterfactuals, risk ratios, odds ratios, causal inference with G-computation, or need help with marginaleffects functions like predictions(), comparisons(), slopes(), hypotheses(), datagrid(), avg_predictions(), avg_comparisons(), avg_slopes(), or plot functions.
Performs epidemiological analyses including disease modeling (SIR/SEIR), outbreak investigation, risk factor identification, incidence/prevalence estimation, and causal inference from observational data; trigger when users discuss disease spread, public health data, or population-level health patterns.
Structured hypothesis generation workflow. Use when: user needs to formulate testable scientific hypotheses from observations, gaps, or preliminary data. NOT for: testing hypotheses or running experiments.
Perform quantitative meta-analysis with effect size calculation, forest plots, funnel plots, and heterogeneity assessment. Use when: user asks to combine results from multiple studies, calculate pooled effect sizes, assess publication bias, or create forest/funnel plots. NOT for: systematic review protocol (use systematic-review) or single-study statistics (use statsmodels-stats).
Scientific visualization via Matplotlib. Use when: user asks for plots, charts, or data visualization. NOT for: interactive dashboards or web-based charts.
Discover patterns, build knowledge graphs, and extract insights from linguistic and historical data
Analyzes environmental and climate data including temperature trends, pollution monitoring, ecological modeling, carbon footprint assessment, and biodiversity metrics; trigger when users discuss climate change, ecosystems, pollutants, or sustainability assessments.
Analyze political data, fact-check claims, and study policy impacts using evidence-based methods
Create publication-quality scientific figures and plots using Python (matplotlib, seaborn, plotly). Supports bar charts, scatter plots, heatmaps, box plots, violin plots, survival curves, network graphs, and more. Use when user asks to plot data, create figures, make charts, visualize results, or generate publication-ready graphics. Triggers on "plot", "chart", "figure", "graph", "visualize", "heatmap", "scatter plot", "bar chart", "histogram".
Advanced statistical testing including hypothesis testing, Bayesian analysis, survival analysis, time series, multivariate methods, and meta-analysis. Use when user needs specific statistical tests beyond basic EDA, power analysis, Bayesian inference, survival curves, time series forecasting, or meta-analysis. Triggers on "hypothesis test", "Bayesian", "survival analysis", "time series", "meta-analysis", "bootstrap", "permutation test", "mixed model", "structural equation".
Statistical analysis via statsmodels. Use when: user asks for regression, hypothesis testing, or time series analysis. NOT for: machine learning models or deep learning.
US Census Bureau data via API. Use when: user asks about US demographics, population, housing, or economic data by geography. NOT for: non-US data or real-time statistics.
Scientific data analysis including data cleaning, exploratory data analysis (EDA), statistical testing, regression, and reporting. Uses Python with pandas, scipy, statsmodels, scikit-learn. Use when user asks to analyze data, clean a dataset, run statistics, do EDA, fit a model, or process CSV/Excel files. Triggers on "analyze this data", "clean my dataset", "run regression", "EDA", "descriptive statistics", "data processing", "correlation analysis".
Comprehensive daily performance review with communication tracking, meeting analysis, output metrics, and focus time monitoring. Your AI performance coach.
Decision tree analysis for complex decision-making across all domains. Use when user needs to evaluate multiple options with uncertain outcomes, assess risk/reward scenarios, or structure choices systematically. Applicable to business, investment, personal decisions, operations, career choices, product strategy, and any situation requiring structured evaluation. Triggers include decision tree, should I, what if, evaluate options, compare alternatives, risk analysis.
Generate images from tables for better readability in messaging apps like Telegram. Use when displaying tabular data.
YouTube Data API v3 analytics toolkit. Analyze YouTube channels, videos, and search results. Use when the user asks to: check YouTube channel stats, analyze video performance, compare channels, search for videos, get subscriber counts, view engagement metrics, find trending videos, get channel uploads, or analyze YouTube competition. Requires a YouTube Data API v3 key from Google Cloud Console.
