Coding & Dev Tools

g1joshi Skill

SQLite embedded database for local storage and mobile apps. Use for lightweight database needs.

g1joshi Skill

Actix Rust web framework with actors and high performance. Use for Rust APIs.

g1joshi Skill

Fiber Express-inspired Go web framework. Use for Go APIs.

g1joshi Skill

Flask Python microframework with blueprints and extensions. Use for lightweight APIs.

g1joshi Skill

Ruby on Rails MVC framework with Active Record and conventions. Use for rapid development.

g1joshi Skill

Tauri lightweight desktop framework with Rust backend. Use for desktop apps.

g1joshi Skill

C# programming for .NET, ASP.NET Core, LINQ, async patterns, and Entity Framework. Use for .cs files.

g1joshi Skill

Delphi Object Pascal for Windows applications. Use for .pas files.

g1joshi Skill

Elixir functional programming with OTP, GenServer, and Phoenix. Use for .ex files.

oimiragieo Bundle

Go programming expert including APIs, gRPC, concurrency, and best practices

oimiragieo Bundle

Linear project management - issues, projects, cycles, and roadmaps. Use for Linear-related tasks like managing issues, tracking sprints, and organizing projects.

oimiragieo Bundle

Unified skill that guides spec creation through structured, interactive process.

oimiragieo Bundle

File system operations guidance - read, write, search, and manage files using Claude Code's built-in tools.

gptomics Bundle

Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.

gptomics Bundle

Build OncoPrint and co-mutation matrix plots from somatic-variant cohorts using ComplexHeatmap, maftools, and comut.py with alteration-type stacking, sample ordering by mutational burden, mutual-exclusivity overlays, and clinical annotation tracks. Use when visualizing per-sample mutation patterns across recurrent driver genes, comparing alteration classes, or identifying mutually-exclusive / co-occurring driver pairs.

gptomics Bundle

Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.

gptomics Bundle

Transcribe DNA to RNA and translate to protein using Biopython, with NCBI codon-table selection, CDS validation, and six-frame ORF finding. Use when converting a CDS or ORF to its amino-acid sequence, selecting a non-standard (mitochondrial, bacterial, ciliate) genetic code, validating a coding sequence, or scanning all reading frames.

gptomics Bundle

Simulate transcription factor perturbation effects on cell state in silico with CellOracle and Dynamo, and predict transcriptional responses to genetic perturbations with GEARS, scGen, and CPA. Covers the direction-not-magnitude principle, local-linear validity, the GRN/velocity error it inherits, baseline discipline (mean and additive baselines), and the validation gap. Use when predicting TF knockout or overexpression effects, ranking driver TFs for fate transitions, or planning perturbation experiments. For GRN construction see multiomics-grn; for experimental Perturb-seq see single-cell/perturb-seq.

gptomics Bundle

Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma, Schwede & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2021 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.

gptomics Bundle

Reconcile gene trees against a species tree under probabilistic models of duplication, transfer, and loss (DTL) using ALE (Szöllősi 2013 amalgamated likelihood), GeneRax (Morel 2020 ML reconciliation), AleRax (Morel 2024 co-estimation), Whale.jl (Bayesian DL+WGD), RANGER-DTL 2 parsimony, NOTUNG, ecceTERA, and Treerecs. Use when inferring ancestral gene-family content, distinguishing duplication from horizontal transfer from differential loss, rooting deep species trees from gene-content signals (STRIDE / Williams 2017 ALE-rooting), counting DTL events per branch, refining noisy gene trees against a species tree, modeling WGD events jointly with DTL, or producing publication-grade gene-family histories for phylogenomic / comparative analyses.

gptomics Bundle

Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.

oimiragieo Bundle

Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.

oimiragieo Bundle

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

g1joshi Skill

Erlang concurrent programming with OTP. Use for .erl files.

g1joshi Skill

F# functional-first programming on .NET. Use for .fs files.

g1joshi Skill

Groovy scripting for JVM, Gradle builds, and Jenkins pipelines. Use for .groovy files.

g1joshi Skill

Kotlin programming for Android, coroutines, and JVM development. Use for .kt files.

g1joshi Skill

Capacitor cross-platform native runtime. Use for web to native.

g1joshi Skill

Selenium browser automation framework. Use for web testing.

g1joshi Skill

CQRS command query responsibility segregation. Use for complex domains.

g1joshi Skill

gRPC high-performance RPC framework with protobuf. Use for service communication.

g1joshi Skill

Saga pattern for distributed transactions. Use for microservices transactions.

g1joshi Skill

CouchDB document database with sync. Use for offline-first apps.

g1joshi Skill

MariaDB MySQL-compatible database with Galera clustering. Use for MySQL-compatible database needs.

g1joshi Skill

Cloudflare CDN, Workers, and edge services. Use for CDN and edge.

g1joshi Skill

Prometheus monitoring and alerting with PromQL. Use for metrics collection.