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1. ▌
   Agent Tools 2 · bg-szy bundle

   Run 250+ AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating Twitter. Triggers: inference.sh, infsh, ai model, run ai, serverless ai, ai api, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok
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   Bio Comparative Genomics Hgt Detection · bg-szy bundle

   Detect horizontal gene transfer events using HGTector, compositional analysis, and phylogenetic incongruence methods. Identify foreign genes in bacterial and archaeal genomes from anomalous composition or unexpected phylogenetic placement. Use when searching for horizontally transferred genes or analyzing genome evolution in prokaryotes.
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   Bio Epitranscriptomics M6anet Analysis · bg-szy bundle

   Detect m6A modifications from Oxford Nanopore direct RNA sequencing using m6Anet. Use when analyzing epitranscriptomic modifications from long-read RNA data without immunoprecipitation.
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   Bio Experimental Design Power Analysis · bg-szy bundle

   Calculates statistical power and minimum sample sizes for RNA-seq, ATAC-seq, and other sequencing experiments. Use when planning experiments, determining how many replicates are needed, or assessing whether a study is adequately powered to detect expected effect sizes.
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   Bio Genome Assembly Long Read Assembly · bg-szy bundle

   De novo genome assembly from Oxford Nanopore or PacBio long reads using Flye and Canu. Produces highly contiguous assemblies suitable for complete bacterial genomes and resolving complex regions. Use when assembling genomes from ONT or PacBio reads.
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   Bio Genome Intervals Coverage Analysis · bg-szy bundle

   Calculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency.
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7. ▌
   Bio Machine Learning Omics Classifiers · bg-szy bundle

   Builds classification models for omics data using RandomForest, XGBoost, and logistic regression with sklearn-compatible APIs. Includes proper preprocessing and evaluation metrics for biomarker classifiers. Use when building diagnostic or prognostic classifiers from expression or variant data.
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8. ▌
   Bio Machine Learning Survival Analysis · bg-szy bundle

   Analyzes time-to-event data using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression with lifelines. Builds survival models from clinical and omics features. Use when predicting patient survival or modeling time-to-event outcomes.
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9. ▌
   Bio Rna Quantification Count Matrix Qc · bg-szy bundle

   Quality control and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE analysis.
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10. ▌
    Bio Single Cell Differential Abundance · bg-szy bundle

    Test whether cell-type proportions or composition changed between conditions in single-cell data using Milo (miloR), scCODA, sccomp, and propeller. Use when comparing cell-type proportions / composition between conditions, asking which populations expanded or contracted with treatment or disease, running neighborhood-level (cluster-free) abundance testing, or guarding against compositional shifts that masquerade as differential expression.
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11. ▌
    Bio Workflows Genome Assembly Pipeline · bg-szy bundle

    End-to-end genome assembly workflow from reads to polished assembly with QC. Supports short reads (SPAdes), long reads (Flye), and hybrid approaches. Use when assembling genomes from raw reads.
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12. ▌
    Bio Workflows Somatic Variant Pipeline · bg-szy bundle

    End-to-end somatic variant calling from tumor-normal paired samples using Mutect2 or Strelka2. Covers preprocessing, variant calling, filtering, and annotation for cancer genomics. Use when calling somatic mutations from tumor-normal pairs.
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13. ▌
    Bio Epitranscriptomics M6a Differential · bg-szy bundle

    Identify differential m6A methylation between conditions from MeRIP-seq. Use when comparing epitranscriptomic changes between treatment groups or cell states.
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14. ▌
    Bio Epitranscriptomics M6a Peak Calling · bg-szy bundle

    Call m6A peaks from MeRIP-seq IP vs input comparisons. Use when identifying m6A modification sites from methylated RNA immunoprecipitation data.
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    Bio Genome Assembly Metagenome Assembly · bg-szy bundle

    Metagenome assembly from long reads using metaFlye and metaSPAdes with binning strategies. Use when reconstructing genomes from microbial communities, recovering metagenome-assembled genomes (MAGs), or resolving strain-level variation in complex samples.
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    Bio Genome Assembly Short Read Assembly · bg-szy bundle

    De novo genome assembly from Illumina short reads using SPAdes. Covers bacterial, fungal, and small eukaryotic genome assembly, as well as metagenome and transcriptome assembly modes. Use when assembling genomes from Illumina reads.
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17. ▌
    Bio Phasing Imputation Reference Panels · bg-szy bundle

    Download, prepare, and manage reference panels for phasing and imputation. Covers 1000 Genomes, HRC, and TOPMed panels. Use when setting up imputation infrastructure or selecting appropriate reference panels for target populations.
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18. ▌
    Chembl Database · bg-szy

    Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50/Ki values, drug mechanisms, or structure searches.
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19. ▌
    Gnomad Database · bg-szy

    Query the Genome Aggregation Database (gnomAD). Use when determining the rarity or allele frequency of specific genetic variants, retrieving gene constraint metrics (pLI, LOEUF) to assess loss-of-function intolerance, finding variants in a genomic region or gene, or querying structural variants. Don't use for analyzing individual patient genomes, tracking somatic mutations in cancer (use COSMIC), or requesting raw sequencing reads (use ENA).
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20. ▌
    Jaspar Database · bg-szy

    Query the JASPAR database for Transcription Factor (TF) binding profiles. Use when retrieving Position Frequency Matrices (PFMs) or Position Weight Matrices (PWMs) for specific TFs, resolving gene symbols to JASPAR Matrix IDs, or getting TF metadata. Supports multiple output formats (MEME, TRANSFAC, PFM, JASPAR, YAML).
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21. ▌
    Pubmed Database 2 · bg-szy

    Search PubMed for scientific literature, including published clinical trials. Fetch abstracts and full text. Link published research to biological databases (gene, protein, nucleotide, PubChem) to discover associations between papers and specific compounds or genes. Verify medical spelling, match raw citations, and cache result sets for bulk processing. Interfaces NCBI E-utilities and PMC BioC APIs.
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22. ▌
    String Database · bg-szy

    Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology. Use when the user asks about interactions between specific proteins, interaction evidence, confidence scores, protein interaction partners, or pathway enrichments.
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23. ▌
    Devops Network Calculator For Azure · bg-szy bundle

    Azure network planning, CIDR calculation, subnet sizing, and best-practices tool. Use this skill whenever the user asks about subnet sizing, CIDR planning, AKS networking, NSG rules, network segmentation, IP address management, VNet planning, address space analysis, overlap detection, or any Azure networking topic. Also trigger when the user mentions network calculator, net-calc, calculate hosts, plan subnets, or asks about Azure network best practices, even if they don't explicitly say 'network calculator'.
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24. ▌
    Bio Experimental Design Multiple Testing · bg-szy bundle

    Applies multiple testing correction methods including FDR, Bonferroni, and q-value for genomics data. Use when filtering differential expression results, setting significance thresholds, or choosing between correction methods for different study designs.
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25. ▌
    Bio Genome Intervals Interval Arithmetic · bg-szy bundle

    Core interval arithmetic operations including intersect, subtract, merge, complement, map, and groupby using bedtools and pybedtools. Use when finding overlapping regions, removing overlaps, combining adjacent intervals, or transferring annotations between interval files.
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26. ▌
    Bio Machine Learning Biomarker Discovery · bg-szy bundle

    Selects informative features for biomarker discovery using Boruta all-relevant selection, mRMR minimum redundancy, and LASSO regularization. Use when identifying biomarkers from high-dimensional omics data.
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27. ▌
    Bio Phasing Imputation Haplotype Phasing · bg-szy bundle

    Phase genotypes into haplotypes using Beagle or SHAPEIT. Resolves which alleles are inherited together on each chromosome. Use when preparing VCF files for imputation, HLA typing, or population genetic analyses requiring phased haplotypes.
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28. ▌
    Bio Rna Quantification Tximport Workflow · bg-szy bundle

    Import transcript-level quantifications from Salmon/kallisto into R for gene-level analysis with DESeq2/edgeR using tximport or tximeta. Use when importing transcript counts into R for DESeq2/edgeR.
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29. ▌
    Bio Comparative Genomics Synteny Analysis · bg-szy bundle

    Analyze genome collinearity and syntenic blocks using MCScanX, SyRI, and JCVI for comparative genomics. Detect conserved gene order, chromosomal rearrangements, and whole-genome duplications. Use when comparing genome structure between species or identifying conserved genomic regions.
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30. ▌
    Bio Data Visualization Multipanel Figures · bg-szy bundle

    Combine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, or matplotlib GridSpec with shared legends and panel labels. Use when combining multiple plots into publication figures.
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31. ▌
    Bio Genome Intervals Proximity Operations · bg-szy bundle

    Find nearest features, search within windows, and extend intervals using closest, window, flank, and slop operations. Use when performing TSS proximity analysis, assigning enhancers to genes, defining promoter regions, or finding nearby genomic features.
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32. ▌
    Bio Systems Biology Flux Balance Analysis · bg-szy bundle

    Perform flux balance analysis (FBA) and flux variability analysis (FVA) on genome-scale metabolic models using COBRApy. Predict growth rates, metabolic fluxes, and optimal resource utilization. Use when predicting metabolic phenotypes or optimizing flux distributions.
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33. ▌
    Bio Temporal Genomics Trajectory Modeling · bg-szy bundle

    Models continuous temporal trajectories from BULK or time-resolved omics where the x-axis is measured experimental time: penalized GAMs (mgcv) for smooth trends and changepoint detection (segmented, ruptures) for abrupt regime shifts. Use when deciding between a smooth GAM and a changepoint model; choosing the GAM distribution (nb() plus a library-size offset for raw counts vs Gaussian on vst/log-CPM); setting the basis-dimension ceiling k below the number of timepoints and letting REML pick wiggliness; handling residual autocorrelation across timepoints with corAR1/bam(rho=); testing whether two conditions' trajectories diverge with an ordered-factor difference smooth; and choosing a changepoint search/cost/penalty (Pelt/Binseg, l2/rbf). Not for single-cell pseudotime (see single-cell/trajectory-inference).
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34. ▌
    Bio Workflows Metabolic Modeling Pipeline · bg-szy bundle

    End-to-end genome-scale metabolic modeling from genome sequence to flux predictions. Covers automated reconstruction with CarveMe, model validation with memote, FBA/FVA analysis, and gene essentiality prediction. Use when building metabolic models or predicting metabolic phenotypes from genomic data.
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35. ▌
    Clinvar Database · bg-szy

    Use when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls for human genomic variants.
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36. ▌
    Ensembl Database · bg-szy

    Query the Ensembl database to resolve gene, transcript, and protein IDs, fetch genomic or protein sequences, retrieve gene structures (exons), and get variant consequence and effect predictions (VEP). Use this skill as a primary ID translator, genomic sequence database and variant effect prediction tool.
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37. ▌
    Pubchem Database · bg-szy

    Query PubChem, search by name/CID/SMILES, retrieve properties, similarity/substructure searches, bioactivity, for cheminformatics. Use when a user asks about a specific chemical, drug, or molecule.
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38. ▌
    Quickgo Database · bg-szy

    Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API. Use this when you need to map genes to biological processes, molecular functions, or cellular components, find genes associated with a specific pathway/GO term, or explore the Gene Ontology hierarchy. Do not use for querying drug targets (use OpenTargets) or mechanistic signaling pathway diagrams (use KEGG).
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39. ▌
    Uniprot Database · bg-szy

    Access protein metadata, function, taxonomy, and sequences across UniProtKB, UniParc, and UniRef. Use when searching for proteins, mapping identifiers, or retrieving functional annotations and publications. Don't use for sequence alignment, protein folding, or sequence similarity search (use specialized skills for those tasks).
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40. ▌
    Bio Data Visualization Heatmaps Clustering · bg-szy bundle

    Create clustered heatmaps with row/column annotations using ComplexHeatmap, pheatmap, and seaborn for gene expression and omics data visualization. Use when visualizing expression patterns across samples or identifying co-expressed gene clusters.
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41. ▌
    Bio Epitranscriptomics Merip Preprocessing · bg-szy bundle

    Align and QC MeRIP-seq IP and input samples for m6A analysis. Use when preparing MeRIP-seq data for peak calling or differential methylation analysis.
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42. ▌
    Bio Phasing Imputation Genotype Imputation · bg-szy bundle

    Impute missing genotypes using reference panels with Beagle or Minimac4. Use when increasing variant density for GWAS, harmonizing data across genotyping platforms, or inferring variants not directly typed in array data.
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43. ▌
    Bio Workflow Management Nextflow Pipelines · bg-szy bundle

    Create scalable, containerized bioinformatics pipelines with Nextflow DSL2 supporting Docker, Singularity, and cloud execution. Use when building portable pipelines with container support, running workflows on cloud platforms (AWS, Google Cloud), or leveraging nf-core community pipelines.
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44. ▌
    Bio Comparative Genomics Ortholog Inference · bg-szy bundle

    Infer orthologous gene groups across species using OrthoFinder and ProteinOrtho. Identify orthologs, paralogs, and co-orthologs for comparative genomics and functional annotation transfer. Use when identifying gene orthologs across species or building orthogroups for evolutionary analysis.
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45. ▌
    Bio Comparative Genomics Positive Selection · bg-szy bundle

    Detect positive selection using dN/dS (omega) tests with PAML codeml and HyPhy. Identify sites and branches under adaptive evolution through codon models and branch-site tests. Use when testing for adaptive evolution in gene families or identifying positively selected sites.
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46. ▌
    Bio Data Visualization Ggplot2 Fundamentals · bg-szy bundle

    Create publication-quality scientific figures with ggplot2 including scatter plots, boxplots, heatmaps, and multi-panel layouts. Use when creating static figures for papers, presentations, or reports in R.
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47. ▌
    Bio Genome Assembly Contamination Detection · bg-szy bundle

    Detect contamination and assess genome quality using CheckM, CheckM2, GTDB-Tk, and GUNC for metagenome-assembled genomes and isolate assemblies. Use when checking assemblies for contamination.
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48. ▌
    Bio Machine Learning Prediction Explanation · bg-szy bundle

    Explains machine learning predictions on omics data using SHAP values and LIME for feature attribution. Identifies which genes or features drive classifier decisions. Use when interpreting biomarker classifiers or understanding model predictions.
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49. ▌
    Bio Population Genetics Association Testing · bg-szy bundle

    Single-variant common-variant GWAS with plink2 --glm (linear/logistic, Firth) and the linear mixed models GEMMA, BOLT-LMM, SAIGE, regenie (SPA). A GWAS statistic is valid only when genotype is independent of unmodeled phenotype drivers after the chosen covariates and random effects, so the engine follows sample structure and case:control imbalance, not taste: PC covariates absorb continuous ancestry but cannot remove relatedness (a covariance structure needing an LMM), genomic inflation above 1 is mostly true polygenic signal not confounding (the LDSC intercept is the diagnostic), LOCO prevents proximal contamination, and SPA/Firth keep the tail calibrated at extreme imbalance and low MAC. Use when running single-variant GWAS, choosing between a GLM and a mixed model, or controlling stratification, relatedness, and case:control imbalance. For rare-variant aggregation (burden, SKAT, SKAT-O, ACAT) see rare-variant-association; fine-mapping and MR see causal-genomics; PRS see clinical-databases/polygenic-risk.
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50. ▌
    Bio Systems Biology Context Specific Models · bg-szy bundle

    Build tissue and condition-specific metabolic models using GIMME, iMAT, and INIT algorithms with expression data constraints. Create models that reflect cell-type specific metabolism. Use when building tissue-specific metabolic models or integrating transcriptomics with FBA.
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51. ▌
    Bio Workflow Management Snakemake Workflows · bg-szy bundle

    Build reproducible bioinformatics pipelines with Snakemake using rules, wildcards, and automatic dependency resolution. Use when creating Python-based workflows, automating multi-step analyses with make-like dependency tracking, or running pipelines on HPC clusters with SLURM.
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52. ▌
    Interpro Database · bg-szy

    Identify domains, families, and sites in proteins; find all proteins in a family or sharing a domain; explore species distribution for a domain; annotate genomes with protein families and GO terms. InterPro combines 14 databases (e.g., Pfam, CDD) into one searchable resource. InterPro-N significantly expands annotation and sequence coverage with deep learning. Includes domain architecture (IDA) search.
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53. ▌
    Reactome Database · bg-szy

    Query the Reactome database (Analysis and Content Services). Use when the user asks about pathway analysis, gene list enrichment, retrieving results by token, finding unmapped or not-found identifiers, mapping identifiers, reaction participants (inputs, outputs), pathway hierarchy (including top-level pathways), diagram export, cross-reference mapping, or searching the knowledgebase.
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54. ▌
    Web Search 2 · bg-szy bundle

    Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
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55. ▌
    Bio Clinical Biostatistics Survival Analysis · bg-szy bundle

    Performs time-to-event analysis for clinical trials including Cox proportional hazards regression with PH diagnostics, restricted mean survival time (RMST) under non-PH, competing risks via Fine-Gray vs cause-specific Cox, weighted log-rank and MaxCombo for non-proportional hazards, recurrent events (Andersen-Gill, PWP, WLW), and interval-censored data. Use when analyzing time-to-event endpoints (OS, PFS, DOR, TTR, TTNT) in oncology or other clinical trials.
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56. ▌
    Bio Data Visualization Genome Browser Tracks · bg-szy bundle

    Generate genome browser visualizations using pyGenomeTracks or IGV batch scripting for publication figures. Use when creating publication figures of genomic regions with multiple data tracks.
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57. ▌
    Bio Data Visualization Volcano Customization · bg-szy bundle

    Create publication-ready volcano plots with custom thresholds, gene labels, and highlighting using ggplot2, EnhancedVolcano, or matplotlib. Use when visualizing differential expression or association results with gene annotations.
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58. ▌
    Bio Differential Expression Batch Correction · bg-szy bundle

    Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), and the Goh 2017 harmonization critique. Use when designing a DE analysis with batch structure, troubleshooting batch-dominated PCA, choosing ComBat vs ComBat-seq, handling unknown batch via SVA, integrating across studies, or deciding when (rarely) to subtract batch.
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59. ▌
    Bio Population Genetics Population Structure · bg-szy bundle

    Analyze population structure using PCA and admixture analysis with PLINK and ADMIXTURE. Identify population clusters, assess ancestry proportions, visualize genetic structure, and choose optimal K for admixture models. Use when analyzing population stratification with PCA or admixture.
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60. ▌
    Bio Population Genetics Selection Statistics · bg-szy bundle

    Detect signatures of natural selection using Fst, Tajima's D, iHS, XP-EHH, and other selection statistics. Calculate population differentiation, test for departures from neutrality, and identify selective sweeps with scikit-allel and vcftools. Use when computing selection signatures like Fst or Tajima's D.
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61. ▌
    Bio Systems Biology Metabolic Reconstruction · bg-szy bundle

    Build genome-scale metabolic models from genome sequences using CarveMe and gapseq for automated reconstruction. Generate draft models ready for curation and analysis. Use when creating metabolic models for organisms without existing models.
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62. ▌
    Bio Population Genetics Scikit Allel Analysis · bg-szy bundle

    Python population genetics with scikit-allel. Read VCF files, compute allele frequencies, calculate diversity statistics, perform PCA, and run selection scans using GenotypeArray and HaplotypeArray data structures. Use when analyzing population genetics in Python.
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63. ▌
    Bio Research Tools Biomarker Signature Studio · bg-szy

    Multi-omic biomarker discovery studio that ingests expression + metadata, performs QC, multi-strategy feature selection, nested CV model training, survival analysis hooks, and SHAP-based interpretation. Use to design translational biomarker panels with documented evidence.
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64. ▌
    Bio Rna Quantification Featurecounts Counting · bg-szy bundle

    Count reads per gene from aligned BAM files using Subread featureCounts. Use when processing BAM files from STAR/HISAT2 to generate gene-level counts for DESeq2/edgeR.
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65. ▌
    Bio Data Visualization Specialized Omics Plots · bg-szy bundle

    Reusable plotting functions for common omics visualizations. Custom ggplot2/matplotlib implementations of volcano, MA, PCA, enrichment dotplots, boxplots, and survival curves. Use when creating volcano, MA, or enrichment plots.
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66. ▌
    Bio Population Genetics Linkage Disequilibrium · bg-szy bundle

    Calculate linkage disequilibrium statistics (r², D'), perform LD pruning for population structure analysis, identify haplotype blocks, and visualize LD patterns using PLINK, scikit-allel, and LDBlockShow. Use when calculating LD or pruning variants.
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67. ▌
    Web Search 3 · bg-szy bundle

    Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
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68. ▌
    Bio Batch Downloads · bg-szy bundle

    Download large datasets from NCBI efficiently using history server, batching, and rate limiting. Use when performing bulk sequence downloads, handling large query results, or production-scale data retrieval.
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69. ▌
    Bio Fragment Analysis · bg-szy bundle

    Extracts cfDNA fragmentomics features (DELFI genome-wide short/long ratios, WPS nucleosome positioning, Griffin GC-corrected accessibility profiles, end-motifs/MDS, OCF) for cancer detection and tissue-of-origin from plasma WGS. Centers on the nuclease-footprint reframe (every feature re-reads one nucleosome object), the mandatory GC correction, and the cross-protocol non-comparability that breaks naive classifiers. Runs FinaleToolkit (real CLI/Python, MIT) and the Griffin Snakemake pipeline; DELFI is a method, not a package. Use when deriving fragment-based signal from cfDNA, choosing a feature family for detection vs subtyping, or diagnosing why a fragmentomic model failed validation.
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70. ▌
    Bio Workflows Cnv Pipeline 2 · bg-szy bundle

    Orchestrates the copy-number pipeline from BAM to segmented, integer-called, annotated CNVs, forking on germline-vs-somatic - CNVkit (somatic exome/panel: coverage -> assay-matched reference/PoN -> fix -> segment -> purity/ploidy-aware call), GATK gCNV (germline rare-CNV cohort), and allele-specific callers (ASCAT/FACETS/PURPLE) for purity/ploidy. Use when committing the build + target/access BED + PoN once (assay-matched), building the reference from normals BEFORE segmenting, fitting purity/ploidy BEFORE integer calls in tumors, centering on the true (non-diploid) mode before GISTIC2 recurrence, or routing cfDNA to ichorCNA. Hands mechanism to the copy-number component skills; not a re-teach of any single step.
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71. ▌
    Bio Data Visualization Interactive Visualization · bg-szy bundle

    Create interactive HTML plots with plotly and bokeh for exploratory data analysis and web-based sharing of omics visualizations. Use when building zoomable, hoverable plots for data exploration or web dashboards.
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72. ▌
    Bio Epitranscriptomics Modification Visualization · bg-szy bundle

    Create metagene plots and browser tracks for RNA modification data. Use when visualizing m6A distribution patterns around genomic features like stop codons.
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73. ▌
    Opentargets Database · bg-szy

    Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.
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74. ▌
    Bio Alignment Sorting · bg-szy bundle

    Sort alignment files by coordinate or read name using samtools and pysam. Use when preparing BAM files for indexing, variant calling, or paired-end analysis.
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75. ▌
    Bio Sequence Similarity 2 · bg-szy bundle

    Find homologous sequences using iterative BLAST (PSI-BLAST), profile HMMs (HMMER), and reciprocal best hit analysis. Use when identifying orthologs, distant homologs, or protein family members where standard BLAST is not sensitive enough.
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76. ▌
    Bio Methylation Methylkit · bg-szy bundle

    Imports Bismark coverage or cytosine-report files into the methylKit object model, then runs the import-to-results spine - filterByCoverage, normalizeCoverage, unite/destrand, calculateDiffMeth, getMethylDiff - for both per-CpG (DMC) and fixed-tile (DMR) differential methylation, plus tileMethylCounts, PCA/correlation/clustering QC, and assocComp/removeComp batch handling. Covers the silent default traps that shape the false-positive rate: overdispersion='none' does no correction while 'MN' forces the F-test (ignoring test='Chisq'), adjust defaults to SLIM not BH, getMethylDiff defaults difference=25/qvalue=0.01, cov.bases=0 admits single-CpG tiles, and pool destroys biological replication. Use when importing bisulfite count tables, filtering/normalizing/uniting methylation samples, running methylKit differential testing, or QC-ing methylomes. For per-site test-choice (count vs continuous) see differential-cpg-testing; for selection-aware region FDR (dmrseq/DSS) see dmr-detection.
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77. ▌
    Bio Alignment Indexing · bg-szy bundle

    Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam. Use when enabling random access to alignment files or fetching specific genomic regions.
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78. ▌
    AI Image Generation 2 · bg-szy bundle

    Generate AI images with FLUX, Gemini, Grok, Seedream, Reve and 50+ models via inference.sh CLI. Models: FLUX Dev LoRA, FLUX.2 Klein LoRA, Gemini 3 Pro Image, Grok Imagine, Seedream 4.5, Reve, ImagineArt. Capabilities: text-to-image, image-to-image, inpainting, LoRA, image editing, upscaling, text rendering. Use for: AI art, product mockups, concept art, social media graphics, marketing visuals, illustrations. Triggers: flux, image generation, ai image, text to image, stable diffusion, generate image, ai art, midjourney alternative, dall-e alternative, text2img, t2i, image generator, ai picture, create image with ai, generative ai, ai illustration, grok image, gemini image
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79. ▌
    AI Video Generation 2 · bg-szy bundle

    Generate AI videos with Google Veo, Seedance, Wan, Grok and 40+ models via inference.sh CLI. Models: Veo 3.1, Veo 3, Seedance 1.5 Pro, Wan 2.5, Grok Imagine Video, OmniHuman, Fabric, HunyuanVideo. Capabilities: text-to-video, image-to-video, lipsync, avatar animation, video upscaling, foley sound. Use for: social media videos, marketing content, explainer videos, product demos, AI avatars. Triggers: video generation, ai video, text to video, image to video, veo, animate image, video from image, ai animation, video generator, generate video, t2v, i2v, ai video maker, create video with ai, runway alternative, pika alternative, sora alternative, kling alternative
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80. ▌
    Remotion Render · bg-szy bundle

    Render videos from React/Remotion component code via inference.sh. Pass TSX code, get MP4. Supports all Remotion APIs: useCurrentFrame, useVideoConfig, spring, interpolate, AbsoluteFill, Sequence. Configurable resolution, FPS, duration, codec. Use for: programmatic video generation, animated graphics, motion design, data-driven videos, React animations to video. Triggers: remotion, render video from code, tsx to video, react video, programmatic video, remotion render, code to video, animated video, motion graphics code, react animation video
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81. ▌
    Bio Alignment Filtering · bg-szy bundle

    Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.
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82. ▌
    Bio Alignment Validation · bg-szy bundle

    Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.
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83. ▌
    Bio Population Genetics Plink Basics 2 · bg-szy bundle

    Manages PLINK genotype filesets - format conversion (VCF, BED/BIM/FAM, PED/MAP, pgen/pvar/psam) and sample/variant QC (missingness, MAF, HWE, sex check, heterozygosity, KING relatedness) with PLINK 1.9 and 2.0. PLINK rewrites allele bookkeeping: PLINK 1.x A1 defaults to the minor allele and is recomputed every load, silently flipping effect-allele meaning unless --keep-allele-order, while PLINK 2.0 tracks explicit REF/ALT. QC order matters (variant before sample missingness), HWE is controls-only in 1.9 but not 2.0, add midp, and differential case/control missingness injects false hits. Use when converting between PLINK formats or running genotype QC before association, structure, or LD analysis. For LD pruning/clumping see linkage-disequilibrium; for GWAS see association-testing; VCF input from variant-calling/vcf-basics.
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84. ▌
    Bio Tcr Bcr Analysis Mixcr Analysis · bg-szy bundle

    Align V(D)J reads and assemble TCR/BCR clonotypes with MiXCR, driven by a chemistry-matched preset. Use when choosing/auditing the preset for a library (5'RACE/template-switch vs multiplex-primer amplicon -> rigid vs floating boundaries; RNA vs gDNA -> --rna/--dna; bulk vs 10x single-cell; UMI vs no-UMI -> tag pattern and barcode collapse; kit presets Takara/NEBNext/QIAseq/BD/MiLaboratory); assembling clonotypes by CDR3 vs VDJRegion; setting the reads-vs-UMI-vs-cell quantitation denominator; exporting native MiXCR fields vs AIRR rearrangement TSV for downstream Immcantation/scirpy/VDJtools; and running alignment/chain-usage QC. Keywords: MiXCR, analyze, align, refineTagsAndSort, assemblePartial, assemble, assembleCells, exportClones, exportAirr, exportQc, CDR3, V(D)J, clonotype, UMI, cell barcode, 10x VDJ, license.
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85. ▌
    AI Image Generation 3 · bg-szy bundle

    AI image generation for agents: text-to-image and prompt-to-image through Luxin's zero-setup hosted runtime. Use when an agent needs a generated image with no provider API key, OAuth, local runtime, or per-provider billing account. Start with the no-spend guide, then create durable hosted images with recoverable jobs, cost receipts, model parameters, stable JSON, and browserless x402 when available.
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86. ▌
    AI Video Generation 3 · bg-szy bundle

    AI video generation for agents through Luxin's zero-setup hosted creative runtime. Use when a prompt should become a durable hosted video without provider credentials, OAuth, local runtime, or per-provider billing. Start with the no-spend guide, choose a video model, and keep model discovery, jobs, costs, URLs, JSON recovery, payments, and feedback in one loop.
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87. ▌
    Twitter Automation 2 · bg-szy bundle

    Automate Twitter/X with posting, engagement, and user management via inference.sh CLI. Apps: x/post-tweet, x/post-create (with media), x/post-like, x/post-retweet, x/dm-send, x/user-follow. Capabilities: post tweets, schedule content, like posts, retweet, send DMs, follow users, get profiles. Use for: social media automation, content scheduling, engagement bots, audience growth, X API. Triggers: twitter api, x api, tweet automation, post to twitter, twitter bot, social media automation, x automation, tweet scheduler, twitter integration, post tweet, twitter post, x post, send tweet
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88. ▌
    Bio Flow Cytometry Doublet Detection · bg-szy bundle

    Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification. Covers FSC-A vs FSC-H singlet discrimination (the Area-Height non-proportionality, not a 1D area gate), FSC-W/SSC width gating, CyTOF Gaussian discrimination parameters (Center/Offset/Width/Residual/Event_length) and DNA intercalator gating, and the residual heterotypic conjugates that survive scatter gating and masquerade as double-positive populations. Use when filtering aggregates before phenotyping, choosing a doublet method for flow vs CyTOF, or diagnosing a suspicious double-positive cluster.
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89. ▌
    Bio Tumor Fraction Estimation · bg-szy bundle

    Estimates tumor fraction (the genome-wide proportion of cfDNA molecules that are tumor-derived, the cfDNA analogue of bulk-tumor purity) from shallow whole-genome sequencing with ichorCNA, an HMM over 1 Mb bins that jointly EM-estimates tumor fraction, ploidy, and subclonal prevalence over a normal/ploidy grid. Encodes the load-bearing reframes: tumor fraction is the quantity that travels across assays and is NOT mutation VAF (clonal-het VAF approximately TF/2), CNA-based estimation has a hard ~3 percent limit-of-detection floor, and near-diploid or copy-neutral-LOH genomes return a falsely low value. Selects the estimator by data type (sWGS to ichorCNA, deep panel to max-VAF, methylation to deconvolution, sub-3 percent to fragmentomics or methylation). Use when quantifying tumor burden from a liquid biopsy, picking a tumor-fraction estimator for a given assay, or reconciling a TF estimate against a panel VAF.
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90. ▌
    Code Review Assistant · bg-szy bundle

    自动代码审查 — PR代码→安全扫描+性能分析+最佳实践建议+评审报告
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91. ▌
    Bio Phasing Imputation Haplotype Phasing 2 · bg-szy bundle

    Estimates haplotype phase from population linkage disequilibrium with SHAPEIT5, SHAPEIT4, Eagle2, or Beagle - turning unphased genotypes (0/1) into phased haplotypes (0|1) for imputation input, compound-heterozygote calls, HLA typing, or population genetics. Covers why statistical phase is an INFERENCE (not a measurement) whose error concentrates at rare variants, why a genome-wide switch-error rate hides catastrophic rare-variant error and must be reported MAC-stratified, the SHAPEIT5 common-scaffold-then-rare design (phase_common, ligate, phase_rare, switch), reference-based vs within-cohort phasing, the build-matched genetic map, chrX male-haploid handling, and the switch-vs-flip-vs-Hamming distinction. Use when phasing genotypes before imputation, for compound-het/ASE/HLA, or benchmarking against trios. Read-backed / molecular phasing (long reads, Hi-C) is long-read-sequencing/haplotype-phasing; panel choice is reference-panels; imputation is genotype-imputation.
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92. ▌
    Bio Microbiome Differential Abundance · bg-szy bundle

    Tests which individual taxa differ between groups on an amplicon ASV/feature table (phyloseq) using compositionally-aware methods - ALDEx2 (Dirichlet-MC CLR, conservative), ANCOM-BC2/ANCOMBC (sampling-fraction bias correction, structural zeros, passed_ss, default p_adj_method=holm), MaAsLin2/MaAsLin3 (multivariable GLM, random effects, prevalence/abundance split), LinDA (CLR mixed-model regression), ZicoSeq (permutation FDR), LEfSe, and q2-composition ancombc. Covers why the hit list depends more on the DA tool than the biology (Nearing benchmark) so the deliverable is a CONSENSUS of >=2 tools, why a relative change is not absolute without a load anchor, the prevalence-filter knob, BH/FDR plus an effect-size floor, and why DESeq2/edgeR misfire here. Use when finding differentially abundant taxa, handling covariates or longitudinal designs, or choosing a method. Whole-community diversity -> diversity-analysis; shotgun DA -> metagenomics/metagenome-visualization; CoDA theory -> metagenomics/abundance-estimation
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93. ▌
    Bio Workflows Atacseq Pipeline · bg-szy bundle

    End-to-end ATAC-seq workflow from FASTQ files to differential accessibility and TF footprinting. Covers alignment, peak calling with MACS3, QC metrics, and optional TOBIAS footprinting. Use when running end-to-end ATAC-seq analysis from FASTQ to differential accessibility.
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94. ▌
    Bio Comparative Genomics Ortholog Inference 2 · bg-szy bundle

    Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.
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95. ▌
    Bio Flow Cytometry Differential Analysis · bg-szy bundle

    Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.
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96. ▌
    Bio Genome Assembly Assembly Qc · bg-szy bundle

    Assess genome assembly quality using QUAST for contiguity metrics and BUSCO for completeness. Essential for evaluating assembly success and comparing assemblers. Use when evaluating assembly completeness and quality.
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97. ▌
    Bio Vcf Basics · bg-szy bundle

    View, query, and understand VCF/BCF variant files using bcftools and cyvcf2. Use when inspecting variants, extracting specific fields, or understanding VCF format structure.
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98. ▌
    Bio Population Genetics Population Structure 2 · bg-szy bundle

    Infers and describes population structure with PCA (plink2 --pca, smartpca/EIGENSOFT, FlashPCA2), model-based clustering (ADMIXTURE, fastSTRUCTURE), FST estimators (Weir-Cockerham vs Hudson), and f-statistics (f3/f4/D via AdmixTools/admixr), plus Python plotting of PCs and Q barplots. Every output is a model-conditioned description of variance, not truth: PCs conflate ancestry with LD/inversions/relatedness/batch, ADMIXTURE Q-values are panel- and K-dependent artifacts, and CV-minimum K is a guide not the true population count. FST must combine SNPs as a ratio of averages (sum numerators / sum denominators), never an average of per-SNP FST; negative per-SNP FST is normal and must not be clamped. f3/f4/D need a block jackknife or the significance is fake. Use when running PCA, ADMIXTURE, FST, or f-statistics on QC'd genotypes. For QC and KING relatedness see plink-basics; for LD pruning see linkage-disequilibrium; for array-based Python pipelines see scikit-allel-analysis.
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99. ▌
    Bio Data Visualization Genome Browser Tracks 2 · bg-szy bundle

    Generate genome browser visualizations using pyGenomeTracks or IGV batch scripting for publication figures. Use when creating publication figures of genomic regions with multiple data tracks.
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100. ▌
     Bio Population Genetics Scikit Allel Analysis 2 · bg-szy bundle

     In-memory Python population genetics with scikit-allel - GenotypeArray/HaplotypeArray/AlleleCountsArray, diversity (pi, theta, Tajima's D), SFS, FST (Weir-Cockerham, Hudson, Patterson), f3/D admixture stats, LD pruning, PCA, and selection scans (iHS, XP-EHH, nSL, Garud H). Nearly every statistic is a ratio or density with one silent denominator bug in two faces: omit is_accessible= and per-base pi/theta divide by total span not accessible bp (deflated 2-5x); average per-SNP FST instead of sum(a)/(sum(a)+sum(b)+sum(c)) and the estimate is rare-variant-biased - scikit-allel returns the (a,b,c) and (num,den) components on purpose to force ratio-of-sums. to_n_alt default fill=0 imputes missing to reference; sfs() is unfolded and wants derived not alt counts; iHS/XP-EHH need phased data and standardization. Use when computing population-genetics statistics in Python, scanning for selection, or building array pipelines. For PLINK QC see plink-basics; for VCF input see variant-calling/vcf-basics.
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