Use facs gating viz style for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

Use gene structure mapper for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

Use idc-index to query and download public cancer imaging data from NCI Imaging Data Commons. Used to access large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Supports metadata querying, in-browser visualization, and license checking.

Statistical visualization library integrated with pandas; use it when you need fast EDA of distributions, relationships, and categorical comparisons (e.g., box/violin/pair plots and heatmaps) with strong default aesthetics on top of matplotlib.

Search and retrieve scientific preprints from arXiv; use it when you need to find papers by keyword/author/category, fetch metadata (abstract, DOI, PDF URL), or download PDFs for offline reading.

Comprehensive analytics tool for forecasting breakthrough therapeutic technologies by integrating multi-dimensional data sources including clinical development pipelines, intellectual property landscapes, and capital mar.

Comprehensive citation management for academic research; use when you need to discover papers (Google Scholar/PubMed), extract/verify metadata (DOI/PMID/arXiv/URL), and produce validated, clean BibTeX for manuscripts.

Access COSMIC to download mutation datasets, query Cancer Gene Census, and retrieve mutational signatures when your genomic analysis requires curated somatic mutation resources.

Use grant gantt chart gen for evidence insight workflows that need structured execution, explicit assumptions, and clear output boundaries.

Produce a structured close-reading report from a paper's full PDF-to-Markdown text (with `## Page XX` pagination and image references) when you need to systematically extract background, research questions, methods, results, limitations, and reproducible experimental details.

Rapidly skim and summarize academic papers (default:PDF-to-Markdown full text with `## Page XX` pagination and image references) and output a structured extensive-reading summary in Markdown when you need to quickly understand research questions, methods, key results, conclusions, and decide whether intensive reading is worthwhile.

Download academic papers from open-access sources when the user provides a DOI/arXiv ID or requests a keyword-based paper search, and return the saved PDF path.

Convert HTML files or URLs to high-fidelity PDFs using Puppeteer; auto-detects or forces RTL for Hebrew/Arabic when RTL content is present.

Import local literature into a managed library; trigger when you need offline deduplication, tagging, and a searchable index.

Convert files and Office documents into clean Markdown when you need LLM-friendly, token-efficient text (e.g., for summarization, search, RAG ingestion, or dataset preparation).

Interactive visualization library for Python. Use it when you need hover tooltips, zoom/pan, selection, animations, or charts embeddable in web pages (e.g., dashboards, exploratory analysis, presentations).

Converts research text into a Mermaid technical roadmap flowchart. Use when the user provides research proposals, experiment designs, or scientific text and asks for a roadmap or flowchart.

Fix garbled text in PDF/SVG vector graphics caused by font encoding issues, making files editable in AI tools. Supports batch processing and JSON export for manual correction.

<!--

Panel data analysis with Python using linearmodels and pandas.

Run regression analyses in Stata with publication-ready output tables.

>-

>-

Explore methodological approaches through structured analysis before planning implementation

Document a recently solved research problem to compound methodological knowledge

Execute research implementation plans efficiently while maintaining estimation quality and finishing features

End-to-end R data analysis workflow from exploration through regression to publication-ready tables and figures.

R-based econometric analysis for academic research. Use when writing R code for panel data, difference-in-differences, instrumental variables, spatial econometrics, or regression analysis. Covers data.table, fixest, sf, modelsummary, and publication-ready outputs.

Run an end-to-end data analysis in R or Python: load, explore, analyze, and produce publication-ready output.

>-

>-

>-

>-

>-

>-

>-

>-

>-

'Unwraps hard-wrapped markdown files so that each sentence ends with a newline instead of mid-sentence line breaks. Joins continuation lines within a paragraph into single lines, then re-breaks at sentence boundaries (period, question mark, exclamation point). Preserves blank lines, headings, fenced code blocks, block quotes, and list items. Use when asked to unwrap text, fix line breaks, reflow sentences, or clean up hard-wrapped markdown or .qmd files.'

'Redistricting analysis in R using the redistverse ecosystem. Use whenever the user is working with redist, redistmetrics, ggredist, geomander, adj, alarmdata, PL94171, censable, easycensus, tinytiger, baf, rict, or redistio. Covers the complete pipeline: Census and spatial data loading, adjacency graph construction, SMC/MCMC simulation, constraints (population balance, county splits, VRA compliance), convergence diagnostics, plan metrics (compactness, partisan fairness, splits), visualization, summary tables, and interactive plan drawing. Invoke whenever the user mentions redistricting, gerrymandering, district plans, simulation ensembles, or any redistverse package by name.'

humanize

Structured methodology for constructing and verifying mathematical proofs in statistical research

Scans notebooks for data file references and verifies each file exists on disk. Use when checking for broken data paths.

Writes academic prose interpreting regression output. Use when describing estimation results in manuscript-ready language.

Guide to Algorithm Visualizer for interactive algorithm exploration

Guide to D3.js for building custom interactive data visualizations

Guide to Apache ECharts for interactive research data dashboards

Guide to Plotly.py for interactive scientific visualizations in Python

Learn causal inference with Python using the Brave and True handbook

Comprehensive Stata reference covering syntax, econometrics, and 20+ packages