Coding & Dev Tools

g1joshi Skill

Ruby programming with blocks, metaprogramming, gems, and Rails conventions. Use for .rb files.

g1joshi Skill

Rust programming with ownership, borrowing, lifetimes, and zero-cost abstractions. Use for .rs files.

g1joshi Skill

Apache Cordova hybrid mobile framework. Use for hybrid apps.

g1joshi Skill

Flutter cross-platform UI toolkit with Dart. Use for mobile/web/desktop.

g1joshi Skill

SwiftUI declarative Apple UI framework. Use for iOS/macOS.

g1joshi Skill

Xamarin cross-platform with .NET. Use for .NET mobile.

g1joshi Skill

Appium mobile app automation. Use for mobile testing.

gptomics Bundle

Computes linkage disequilibrium (r2, D', composite Rogers-Huff r2), prunes correlated variants, clumps GWAS summary statistics to lead SNPs, and defines haplotype blocks with PLINK 1.9/2.0 and scikit-allel. r2 and D' answer different questions - r2 (= chi2/N) is the tagging and GWAS-power currency, D' marks observed recombination and is upward-biased for rare variants. PLINK 2.0 has no bare --r2 (split into --r2-phased and --r2-unphased); pruning (--indep-pairwise, genotype-blind) and clumping (--clump, p-value-aware) are distinct operations that are constantly confused. The clumping or fine-mapping LD reference must be ancestry-matched or it fails silently into false credible sets. Use when calculating LD, pruning variants for PCA or structure, clumping GWAS hits, or selecting tag SNPs. For QC see plink-basics; for PCA see population-structure; fine-mapping is causal-genomics/fine-mapping.

gptomics Bundle

Trim PCR primers from aligned reads in amplicon-panel BAMs using samtools ampliconclip. Use when processing SARS-CoV-2 ARTIC, hereditary cancer panels, ctDNA hot-spot panels, or any amplicon assay where primer-derived bases would falsely confirm reference at primer footprints.

gptomics Bundle

Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normalization. Use when measuring splice-site usage or isoform inclusion ratios from short-read RNA-seq.

gptomics Bundle

Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.

gptomics Bundle

Computes sample size and power for clinical trials including continuous, binary, and time-to-event endpoints; superiority, non-inferiority, and equivalence designs; FDA 2016 non-inferiority margin selection with M1/M2 framework; Schoenfeld 1981 and Lakatos 1988 for survival; Schuirmann TOST and 80-125% bioequivalence; minimum clinically important difference (MCID) vs δ distinction. Use when justifying trial size in protocol or SAP per CONSORT 2025 item 16a.

gptomics Bundle

Detect introgression and admixture between species or populations using Dsuite (Malinsky 2021 fast D-statistics), Patterson's D / ABBA-BABA test (Green 2010; Durand 2011), f4-ratio and f-branch statistic (Malinsky 2018), TreeMix (Pickrell & Pritchard 2012), HyDe (Blischak 2018), QuIBL (Edelman 2019), sprime (Browning 2018), Twisst (Martin 2017), PhyloNet (Than 2008) for explicit phylogenetic networks, and qpAdm / qpGraph (Patterson 2012). Distinguish introgression from incomplete lineage sorting (ILS), ancestral structure, ghost-lineage admixture, and rate variation. Use when testing inter-species gene flow, dating admixture events, identifying introgressed segments, building phylogenetic networks for reticulate evolution, or applying the ABBAclustering (Koppetsch-Malinsky-Matschiner 2024) framework for divergent-species gene flow.

gptomics Bundle

Estimates cell-type composition from bulk DNA methylation and uses it to defuse the single biggest EWAS confounder. Covers reference-based deconvolution (Houseman constrained-projection, minfi estimateCellCounts2 with FlowSorted.Blood.EPIC + IDOL-optimized libraries, EpiDISH RPC/CBS/CP, 12-cell extended, cord-blood nRBC references, EpiSCORE/hepidish for solid tissue), reference-free correction (ReFACTor, RefFreeEWAS, SVA), using fractions as covariates vs the compositionality/collinearity trap, and cell-type-resolved EWAS (CellDMC, TCA, TOAST, omicwas, HIRE). Use when estimating blood/tissue cell fractions, adjusting an EWAS for composition, choosing a deconvolution reference, or attributing a methylation signal to a cell type. For the EWAS confounder-vs-mediator decision see ewas-design; for the IEAA cell-count adjustment of DNAm age see epigenetic-clocks; for clean beta input see array-preprocessing.

oimiragieo Bundle

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

oimiragieo Bundle

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.

oimiragieo Bundle

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

oimiragieo Bundle

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

g1joshi Skill

Locust load testing in Python. Use for load testing.

g1joshi Skill

pytest Python testing framework with fixtures. Use for Python testing.

g1joshi Skill

TestNG Java testing framework. Use for Java testing.

g1joshi Skill

Vitest fast Vite-native testing. Use for Vite projects.

g1joshi Skill

DataGrip database IDE from JetBrains. Use for database management.

g1joshi Skill

IntelliJ IDEA JetBrains IDE with smart completion. Use for JVM development.

g1joshi Skill

PhpStorm PHP IDE with debugging. Use for PHP development.

g1joshi Skill

Prettier code formatter for consistent style. Use for formatting.

g1joshi Skill

RubyMine Ruby IDE with Rails support. Use for Ruby development.

g1joshi Skill

WebStorm JavaScript IDE with debugging. Use for web development.

g1joshi Skill

Cursor AI editor with context-aware coding. Use for AI-assisted development.

g1joshi Skill

Apache HBase wide-column store on Hadoop. Use for big data.

g1joshi Skill

MySQL relational database with InnoDB, replication, and stored procedures. Use for MySQL operations.

g1joshi Skill

Neo4j graph database with Cypher query language. Use for graph-based data.

g1joshi Skill

Redis in-memory cache, pub/sub, streams, and data structures. Use for caching and real-time data.

g1joshi Skill

Homebrew macOS/Linux package manager. Use for system tools.

g1joshi Skill

Axum ergonomic Rust web framework with tower. Use for Rust APIs.

aradotso-trending-skills Skill

Install and use the 毛选.skill cognitive framework for Claude Code — applies Mao Zedong's strategic mental models (contradiction analysis, protracted war, rural encirclement, united front) to help analyze problems, devise strategies, and cut through complexity.