Data & Analytics

Generate leave-one-out sensitivity analysis plots for meta-analysis. Input is a CSV file containing meta-analysis data; outputs are a sensitivity forest plot (PNG) and a sensitivity data table (CSV) showing pooled effect estimates after excluding each study in turn.

Screen full-text papers against inclusion/exclusion criteria, with optional PubMed metadata check using PMID. Use when the user needs to evaluate a paper for a meta-analysis.

Draw ROB2 risk-of-bias plots, including a Traffic Light Plot and a Summary Bar Plot. Input is a CSV file with ROB2 assessments for each study; output are two PNG plot files.

Generate radial plots (Radial Plot/Galbraith Plot) for heterogeneity analysis. Visually assess heterogeneity across studies by displaying the relationship between standardized effect sizes and precision. Input: Meta-analysis data in CSV format; Output: Radial plot PNG and data CSV.

Generates PI(E)COS structure (Population, Intervention, Comparator, Outcomes, Study Design) from Meta-analysis or study titles. Use when the user wants to extract these elements from a title.

Generate Meta-analysis funnel plots and perform publication bias testing. Takes CSV file with Meta-analysis data as input, outputs funnel plot PNG, Egger test and Begg test results.

Generate forest plots for meta-analysis of survival data. Input is a CSV file containing study names, HR and 95% confidence intervals, output forest plot PNG and data table CSV. Supports both R and Python scripts.

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.

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.

Analyzes the feasibility of a proposed Meta-analysis topic by searching for existing Meta-analyses and Clinical Trials on PubMed/ClinicalTrials.gov. Use when you need to evaluate if a topic is viable for a new Meta-analysis.

Generates scientifically sound inclusion and exclusion criteria for Meta-Analysis based on a given title or keywords. Use when user wants to design eligibility criteria for a systematic review or meta-analysis.

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.

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 f...

Screens research papers based on title/abstract and inclusion criteria, providing a structured Yes/No/Maybe decision. Use when you need to filter literature for meta-analysis or systematic reviews.

> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills) You are **Mendelian Randomisation**, a specialised ClawBio agent for causal inference from GWAS summary statistics. Your role is to run two-sample MR with multiple estimators and a complete sensitivity analysis panel.

Generates Mermaid flowchart code and visual diagrams for pathophysiological.

A low-level plotting library for comprehensive customization. Use when fine-grained control over every plot element is needed, creating new types of charts, or integrating into specific scientific workflows. Can export to PNG/PDF/SVG for publication. For quick statistical charts, use seaborn; for interactive charts, use plotly; for journal-style, publication-ready multi-panel charts, use scientific-visualization.

Process, clean, and compare mass spectrometry (MS/MS) spectra with Matchms; use when you need reproducible spectral filtering and similarity scoring for metabolomics workflows.

Filter compound libraries based on Lipinski's Rule of Five for drug-likeness.

This skill is applicable when using LaminDB. LaminDB is an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR-compliant. It is suitable for managing biological datasets (scRNA-seq, spatial transcriptomics, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakes, or ensuring data lineage and reproducibility in biological research. It covers data management, annotation,...

Use lab budget forecaster for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

Digital pathology image processing toolkit for whole slide images (WSI). Supports WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.

Professional beautification tool for gene expression heatmaps, automatically adds clustering trees, color annotation tracks, and intelligently optimizes label layout.

A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML, reference sequence verification, or fragment processing.