Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Extracts and summarizes key takeaways from documents, meeting notes, articles, and other text content. Use when the user asks for summaries, bullet points, main points, highlights, or a TL;DR of any document or body of text. Produces structured outputs such as numbered lists, executive summaries, and action items. Supports configurable output formats including JSON export for downstream use.
Converts LabArchives notebook data, entry metadata, and authorized ELN exports into manuscript-ready academic writing outputs such as Methods sections, data-availability statements, reproducibility appendices, experiment timelines, and submission support notes. Optional bundled scripts can be used to collect or validate source notebook data before writing.
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.
Extract experimental models, experimental methods, and biomarker information from paper Markdown (typically produced by PDF-to-Markdown tools) when a user provides paper Markdown and needs a structured, evidence-backed summary (1 Markdown + 3 CSVs).
Import local literature into a managed library; trigger when you need offline deduplication, tagging, and a searchable index.
Generate statistics for publication-year and journal distributions from local references or PDFs; use when you need standardized Year/Journal tables and a summary without any network access.
Interpret figures in academic papers and their captions when the input is a PDF-to-Markdown document with page markers and image links, producing a structured Markdown report for extracting variables, trends, and conclusions.
Generate Baujat plots for heterogeneity analysis. Identify studies that contribute most to the overall meta-analysis results and heterogeneity, helping discover potential outlier studies. Input meta-analysis data CSV, output Baujat plot PNG and contribution data CSV.
Generate meta-analysis forest plots for binary classification data. Input is a CSV file containing study names, event counts and sample sizes for experimental and control groups. Output includes forest plot PNG and data table CSV.
Generate forest plots for meta-analysis of continuous data. Input a CSV file containing study names, means, standard deviations, and sample sizes for experimental and control groups. Output forest plot PNG and data table CSV.