Processing
Data transformation and parsing
programmatic-eda
by nimrodfisher
Systematic exploratory data analysis following best practices. Use when analyzing any dataset to understand structure, identify data quality issues (duplicates, missing values, inconsistencies, outliers), examine distributions, detect correlations, and generate visualizations. Provides comprehensive data profiling with sanity checks before analysis.
analysis-assumptions-log
by nimrodfisher
Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
bio-atac-seq-atac-peak-calling
by GPTomics
Call accessible chromatin regions from ATAC-seq data using MACS3 with ATAC-specific parameters. Use when identifying open chromatin regions from aligned ATAC-seq BAM files, different from ChIP-seq peak calling.
ads-youtube
by AgriciDaniel
YouTube Ads specific analysis covering campaign types, creative quality, audience targeting, and measurement. Evaluates video ad performance across skippable, non-skippable, bumper, Shorts, and Demand Gen formats. Use when user says "YouTube Ads", "video ads", "pre-roll", "bumper ads", "YouTube campaign", or "Shorts ads".
data-pipeline
by jmagly
Data dictionary: .aiwg/marketing/data/dictionary.md
markitdown
by brycewang-stanford
Convert various file formats (PDF, Office documents, images, audio, web content, structured data) to Markdown optimized for LLM processing. Use when converting documents to markdown, extracting text from PDFs/Office files, transcribing audio, performing OCR on images, extracting YouTube transcripts, or processing batches of files. Supports 20+ formats including DOCX, XLSX, PPTX, PDF, HTML, EPUB, CSV, JSON, images with OCR, and audio with transcription.
react
by lobehub
React component development guide. Use when working with React components (.tsx files), creating UI, using @lobehub/ui components, implementing routing, or building frontend features. Triggers on React component creation, modification, layout implementation, or navigation tasks.
github-auto-star
by cat-xierluo
"从内容（文章、截图、文字）中自动提取 GitHub 仓库并 star。使用场景：(1) 用户要求分析内容并 star 相关 GitHub 项目，(2) 处理提到工具或库的文章/博客，(3) 分析包含技术引用的截图，(4) 设置定时任务自动发现并 star 仓库。"
Full-empirical-analysis-skill
by brycewang-stanford
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. Defaults to economics empirical-paper style (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. Also covers two parallel domain modes that share the same 8-step scaffolding — Mode A — Epidemiology / public health (target-trial emulation via zepid / hand-rolled pandas, IPTW + g-formula + TMLE doubly-robust triplet via zepid / econml / lifelines, Mendelian randomization via pymr / mrtool (or rpy2 → MendelianRandomization/TwoSampleMR), KM / AFT / Cox survival via lifelines, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and Mode B — ML causal inference (DML via econml.dml / doubleml, S/T/X/R/DR meta-learners via econml.metalearners / causalml, causal forest via econml.grf / causalml, Dragonnet / TARNet / CEVAE neural causal via causalml, BCF via pymc-bart / bcf-py, matrix completion, CATE distribution + policy tree via econml.policy / policytree-py, off-policy evaluation, conformal causal via mapie, fairness audit via fairlearn, DAG learning via causal-learn / cdt / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper references/ files for variant-specific patterns. Use when the user asks for a complete empirical analysis in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".
R-optimizer
by brycewang-stanford
R语言实证分析优化Skill。优化R代码效率、处理大规模面板数据、加速回归计算（并行化、向量化、向量化）。触发词：R语言优化/R加速/R性能优化/大规模数据处理/R optimization
data-fetcher
by brycewang-stanford
Fetch economic data from FRED, World Bank, BLS, OECD, and Yahoo Finance
lifelines
by brycewang-stanford
Complete survival analysis library in Python. Handles right-censored data, Kaplan-Meier curves, and Cox regression. Standard for clinical trial analysis and epidemiology.
gwas-database
by jaechang-hits
"NHGRI-EBI GWAS Catalog REST API for SNP-trait associations from published genome-wide association studies. Query studies, associations, variants, traits, genes, and summary statistics. Build polygenic risk score candidates, analyze variant pleiotropy, download summary statistics for Manhattan plots. No authentication required."
vibe
by foryourhealth111-pixel
Vibe Code Orchestrator (VCO) routes development tasks by grade and coordinates tool workflows for implementation, debugging, review, and planning.
bio-sam-bam-basics
by GPTomics
View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.
bio-alignment-io
by GPTomics
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
bio-causal-genomics-colocalization-analysis
by GPTomics
Test whether two traits share a causal variant at a genomic locus using Bayesian colocalization with coloc. Computes posterior probabilities for shared vs distinct causal variants between GWAS and eQTL signals. Use when determining if a GWAS signal and an eQTL share the same causal variant.
bio-clinical-databases-clinvar-lookup
by GPTomics
Query ClinVar for variant pathogenicity classifications, review status, and disease associations via REST API or local VCF. Use when determining clinical significance of variants for diagnostic or research purposes.
bio-causal-genomics-mediation-analysis
by GPTomics
Decompose genetic effects into direct and indirect paths through mediating variables using the mediation R package. Tests whether gene expression, methylation, or other molecular phenotypes mediate the effect of genetic variants on disease. Use when testing whether a molecular phenotype mediates the genotype-to-phenotype relationship.
bio-splicing-qc
by GPTomics
Assesses RNA-seq data quality for splicing analysis including junction saturation curves, splice site strength scoring, and junction coverage metrics using RSeQC. Use when evaluating data suitability for splicing analysis or troubleshooting low event detection.
bio-causal-genomics-mendelian-randomization
by GPTomics
Estimate causal effects between exposures and outcomes using genetic variants as instrumental variables with TwoSampleMR. Implements IVW, MR-Egger, weighted median, and MR-PRESSO methods for robust causal inference from GWAS summary statistics. Use when testing whether an exposure causally affects an outcome using genetic instruments.
bio-clinical-databases-dbsnp-queries
by GPTomics
Query dbSNP for rsID lookups, variant annotations, and cross-references to other databases. Use when mapping between rsIDs and genomic coordinates or retrieving basic variant information.
bio-atac-seq-motif-deviation
by GPTomics
Analyze transcription factor motif accessibility variability using chromVAR. Use when identifying which TF motifs show variable accessibility across samples or conditions in ATAC-seq data.
cross-conversation-project-manager
by OneWave-AI
Maintain project state across MULTIPLE conversations over days/weeks. Track tasks, decisions, blockers, resources. Auto-update when project mentioned. Generate status reports and proactive reminders.