데이터 분석, 머신러닝, LLM 앱, 프롬프트 설계 (9.1천건)
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, process FASTQ/BAM/VCF, for developing and executing genomics pipelines.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generate...
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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...
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Transform, clean, reshape, and preprocess data using pandas and numpy. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Perform statistical tests, hypothesis testing, correlation analysis, and multiple testing corrections using scipy and statsmodels. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
NGS analysis toolkit. Used for BAM to bigWig conversion, quality control (correlation, PCA, fingerprint plots), heatmaps/feature plots (TSS, peaks), suitable for ChIP-seq, RNA-seq, ATAC-seq visualization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
ETE (Environment for Tree Exploration) toolkit for phylogenetic and hierarchical tree analysis; use it when you need to parse/manipulate Newick/NHX trees, detect duplication/speciation events, integrate NCBI taxonomy, and render publication-quality figures.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
A Python library for reading, writing, and analyzing geospatial vector data; use it when you need spatial operations (buffer/overlay/join), CRS reprojection, or map visualization on formats like Shapefile/GeoJSON/GeoPackage or PostGIS.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use lab budget forecaster for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Constraint-based reconstruction and analysis (COBRA) for metabolic models; use when you need to simulate growth/production, analyze flux ranges, or run knockout and medium studies from SBML/JSON/YAML models.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when refactoring research code for publication, adding documentation to existing analysis scripts, creating reproducible computational workflows, or preparing code for sharing with collaborators. Transforms research code into publication-ready, reproducible workflows. Adds documentation, implements error handling, creates environment specifications, and ensures computational reproducibility for scientific publications.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Process, clean, and compare mass spectrometry (MS/MS) spectra with Matchms; use when you need reproducible spectral filtering and similarity scoring for metabolomics workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze data with `forest-plot-styler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate Circos configuration files for circular genomics data visualization. Supports genomic variations (SNPs, CNVs, structural variants), cell-cell communication networks, and custom track configurations for publication-ready circular plots.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Parse Flow Cytometry Standard (FCS) files v2.0–3.1 and extract events/metadata for preprocessing workflows (e.g., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry files).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use facs gating viz style for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.