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".
# Regulatory Submission — FDA/EMA Dossier Structure
Orchestrates a systematic review and meta-analysis workflow following PRISMA 2020 guidelines, from protocol development through multi-database search, screening, data extraction, and evidence synthesis. Use when conducting evidence-based reviews, meta-analyses, or scoping reviews. NOT for single-study analysis or narrative literature surveys.
Machine learning pipeline for scientific research including data preprocessing, feature engineering, model selection, training, evaluation, and interpretation. Covers supervised/unsupervised learning, deep learning, cross-validation, hyperparameter tuning, and model explainability. Use when user asks to build a predictive model, classify data, cluster samples, do feature selection, or apply ML to research data. Triggers on "machine learning", "classification", "clustering", "random forest", "neural network", "deep learning", "predict", "feature selection", "cross-validation", "train model".
Machine learning with scikit-learn. Use when: classification, regression, clustering, dimensionality reduction, model evaluation, feature engineering. NOT for: deep learning (use transformers/pytorch), time series forecasting (use statsmodels), big data (use spark).
HuggingFace Transformers for model inference. Use when: text classification, NER, question answering, summarization, embeddings, zero-shot classification. NOT for: training large models (use cloud), simple regex/rule-based tasks, production serving at scale (use vLLM).
Best practices for polars data processing with dataframely. Covers definitions of Schema and Collection, usage of .validate() and .filter(), type hints, and testing. Use when writing or modifying code involving dataframely or polars data frames.
Track Clawdbot AI model usage and estimate costs. Use when reporting daily/weekly costs, analyzing token usage across sessions, or monitoring AI spending. Supports Claude (opus/sonnet), GPT, and Codex models.
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.
Monitor Minimax Coding Plan usage to stay within API limits. Fetches current usage stats and provides status alerts.
Designs refreshable Excel dashboards (Power Query + structured tables + validation + pivot reporting). Use when you need a repeatable weekly KPI workbook that updates from files with minimal manual work.
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.
