Data & Analytics

jiayaoqijia Bundle

Multi-repo GitHub management. Cross-repo issues, Projects v2 boards, team workload analytics, milestone tracking, PR pipelines, and release coordination.

jiayaoqijia Bundle

Export a portfolio snapshot with balances and valuations to CSV.

jiayaoqijia Bundle

DefiLlama Yield Intelligence

jiayaoqijia Bundle

Crypto GitHub Analytics

jiayaoqijia Bundle

Ethereum Transaction Analyzer

jiayaoqijia Bundle

Query Polymarket prediction market data via The Graph subgraphs + Polymarket REST APIs (Gamma + CLOB) — 35 tools for market search, live prices, on-chain analytics, trader P&L, open interest, resolution status, and CLOB V2 builder attribution.

theneoai Bundle

Expert-level Data Analyst skill covering SQL analysis, Python/pandas data manipulation, statistical analysis, A/B test design and interpretation, business intelligence, dashboard design, and data storytelling

theneoai Bundle

Senior Logistics Manager with 12+ years optimizing supply chain operations, transportation networks, and distribution systems. Expert in WMS, TMS, network optimization, and 3PL management. Managed $200M+ logistics spend, achieved 15% cost reduction through optimization. CSCMP, APICS certified. Use when: logistics management, supply chain optimization, warehouse operations, transportation

fridrichmethod Bundle

Joint genotype a cohort of per-sample gVCFs with GATK (HaplotypeCaller -ERC GVCF -> GenomicsDBImport or CombineGVCFs -> GenotypeGVCFs) or GLnexus for DeepVariant gVCFs, producing a squared-off sample-by-site genotype matrix. Use when deciding between joint genotyping and merging single-sample callsets (never bcftools merge as absent==hom-ref), choosing GenomicsDBImport vs CombineGVCFs by cohort size and memory, solving the N+1 problem so a new sample does not force re-calling everyone, understanding cohort rescue of low-coverage het sites, handling the spanning-deletion star allele and GQ/PL recomputation at the joint step, scaling to biobank cohorts by interval sharding, or picking DeepVariant+GLnexus over the GATK path on throughput. Not for single-sample calling (see variant-calling/gatk-variant-calling) or VQSR/hard-filter mechanism (see variant-calling/filtering-best-practices).

fridrichmethod Bundle

Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

fridrichmethod Bundle

Compute and interpret VCF quality-control metrics (Ti/Tv, het/hom, novel/known, missingness, HWE, contamination, relatedness) with bcftools stats, vcftools, plot-vcfstats, and identity tools (somalier, peddy, KING). Use when judging whether a callset is trustworthy, diagnosing a low Ti/Tv or outlier het/hom sample, deciding whether an HWE deviation is error or biology, screening a cohort for sample swaps/contamination/wrong-sex before analysis, or comparing call sets before and after filtering. Not for applying filters (see variant-calling/filtering-best-practices) or normalizing representation (see variant-calling/variant-normalization).

fridrichmethod Bundle

Handles BED-format genomic intervals (BED3 through BED12, narrowPeak/broadPeak) and the coordinate-system substrate the whole interval category rests on, with bedtools (CLI) and pybedtools/pyranges/pandas (Python). Covers the 0-based half-open vs 1-based-closed convention boundary and the start-1/end-unchanged conversion, the silent failures (chrom-name mismatch, CRLF, lexicographic-vs-version sort under -sorted), genome/chrom.sizes generation, sorting contracts, BED12 block invariants, validation, makewindows, cross-assembly liftover (liftOver/CrossMap), and BED<->VCF/BAM/FASTA conversion. Use when reading, creating, validating, sorting, lifting between genome builds, or converting interval files, preparing inputs for bedtools/tabix/bigBed, or debugging an off-by-one or empty-overlap result.

fridrichmethod Bundle

Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, biomaRt, mygene, pyensembl, and Ensembl REST. Encodes Ensembl version stripping with GENCODE _PAR_Y preservation, the Ziemann 2016 Excel autocorrect debacle and Bruford 2020 HGNC renames (SEPT*->SEPTIN*, MARCH*->MARCHF*, MARC*->MTARC*, DEC1->DELEC1), OCT4/POU5F1 alias resolution, biomaRt archive endpoints for release pinning, the `filters` (plural) gotcha, MANE Select for clinical reporting, cross-species orthology via Ensembl Compara / OMA / OrthoDB, and tx2gene construction for tximport. Use when converting gene IDs across systems, handling renamed symbols, building tx2gene, pinning to a specific Ensembl release for reproducibility, or mapping cross-species orthologs.

fridrichmethod Bundle

Generates and interprets per-file and cross-sample QC reports from FASTQ data with FastQC, falco, and MultiQC, covering Phred quality, per-base composition, GC, duplication, overrepresented sequences, and adapter content. Use when performing initial QC on raw sequencing reads, validating preprocessing, or judging a multi-sample cohort for outliers and batch effects. For long reads use NanoPlot; for adapter/quality remediation route to adapter-trimming, quality-filtering, or fastp-workflow.

fridrichmethod Bundle

Call germline SNPs and indels from a BAM/CRAM with bcftools mpileup and call, and select the right calling engine for the job. Use when generating a VCF from aligned reads, choosing between bcftools, GATK HaplotypeCaller, DeepVariant, and DRAGEN, setting ploidy for haploid/organelle/polyploid/sex-chromosome calling, or deciding whether pileup-based calling is good enough versus a local-reassembly caller for indels and difficult regions. Not for cohort joint genotyping (see variant-calling/joint-calling), GATK-specific workflows (see variant-calling/gatk-variant-calling), deep-learning calling (see variant-calling/deepvariant), or somatic/low-VAF detection.

fridrichmethod Bundle

Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.

theneoai Bundle

A senior logistics algorithm engineer specializing in vehicle routing (VRP/VRPTW), warehouse optimization, facility location, network design, and real-time dispatch. A senior logistics algorithm engineer specializing in vehicle routing (VRP/VRPTW), warehouse... Use when: logis...

theneoai Bundle

Elite health informatics specialist specializing in EHR optimization, clinical decision support, health data analytics, and interoperability. Bridges clinical practice and information technology to improve patient outcomes through data-driven solutions.

fridrichmethod Skill

Open-source FAIR biology data framework. Version artifacts (AnnData, DataFrame, Zarr), track lineage, validate via ontologies (Bionty), query datasets. Integrates with Nextflow, Snakemake, W&B, scVI. For scRNA-seq use scanpy; for ontology lookups use bionty.

fridrichmethod Bundle

Filters germline and somatic variant callsets at the site and genotype level with GATK VQSR (VQSLOD, truth-sensitivity tranches), VETS/ScoreVariantAnnotations, NVScoreVariants, hard filters with per-annotation thresholds, and bcftools/cyvcf2 expressions, plus Ti/Tv-based QC. Use when deciding between VQSR, hard filtering, and ML recalibration by cohort size and platform, setting SNP vs indel thresholds, replicating the missing-annotation-passes rule so hom-alt sites survive, applying genotype-level GQ/DP filters, or validating filter impact. Not for VCF normalization (see variant-calling/variant-normalization) or summary statistics (see variant-calling/vcf-statistics).

fridrichmethod Bundle

GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.

fridrichmethod Bundle

Combine, split, sort, intersect, and subset VCF/BCF files with bcftools merge, concat, isec, sort, view, and reheader. Use when merging different samples into a cohort VCF, concatenating per-chromosome or per-region call sets for the same samples, intersecting or complementing call sets from different callers, subsetting samples/regions, harmonizing sample names and

fridrichmethod Bundle

Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACETS, Sequenza, and other callers. Covers genome-wide and per-chromosome log2 scatter plots, B-allele-frequency/minor-allele-fraction tracks, ideograms, cohort heatmaps, circos views, and caller-native plots. Use when creating publication CNV figures, choosing which plot answers a given question, diagnosing a wrong diploid baseline visually, displaying loss of heterozygosity, or deciding what depth-only plots cannot reveal.

fridrichmethod Bundle

Builds publication-ready tables - descriptive Table 1, regression and differential-expression result tables, and supplementary tables - with gtsummary, gt, flextable, and kableExtra (R) or great_tables, pandas, and tableone (Python), choosing the right statistics and the right export format. Use when making a Table 1, exporting a formatted results table for a paper, or writing a gene-symbol-safe supplementary table.

fridrichmethod Bundle

Extracts and assigns COSMIC v3.4 mutational signatures (86 SBS / 11 DBS / 18 ID / 21 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.

fridrichmethod Bundle

Builds and manages DIA spectral libraries as peptide query parameters (precursor m/z, a few fragment m/z plus relative intensities, normalized RT, optional CCS), covering experimental DDA, chromatogram, and in-silico predicted libraries via Koina-served Prosit, AlphaPeptDeep, MS2PIP, and DeepLC, with iRT/CiRT RT calibration, NCE tuning, format conversion (DIA-NN tsv/speclib/parquet, OpenSWATH pqp/TraML, Spectronaut, blib/dlib/elib), and library QC/merge. Use when generating, calibrating, converting, or merging a spectral library to drive a DIA search. Running the actual DIA search is dia-analysis; building from DDA identifications depends on peptide-identification; modified-peptide libraries route to ptm-analysis; quantifying the result is quantification.

fridrichmethod Bundle

Turns raw Illumina Infinium methylation BeadChip IDATs (450K, EPIC, EPICv2) into a defensible beta/M matrix with sesame (openSesame/SigDF) or minfi (RGChannelSet -> MethylSet -> GenomicRatioSet). Covers Type I vs Type II probe chemistry and why raw Type II beta is compressed, the signal-to-beta math (beta = M/(M+U+100)) and M-value logit, detection-p / pOOBAH masking including the out-of-band deletion-artifact catch, dye-bias correction, and the normalization decision (noob, funnorm, quantile, SWAN, BMIQ, dasen, sesame QCDPB). Use when reading IDATs, choosing a normalization for a 450K/EPIC/EPICv2 cohort, deciding beta vs M, masking failed probes, or producing the corrected matrix before testing. For probe/sample filtering, EPICv2 replicate collapse, and sample-identity QC see array-qc-filtering; for native long-read 5mC see long-read-sequencing/nanopore-methylation (a different platform).

fridrichmethod Bundle

High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.

fridrichmethod Bundle

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.

fridrichmethod Bundle

Loads mass-spectrometry data into Python/R and strips the search engine's bookkeeping before any number is trusted -- removes decoys (REV__/Reverse), contaminants (CON__/Potential contaminant), Only-identified-by-site groups, and resolves semicolon razor/leading protein-ID ambiguity in MaxQuant proteinGroups.txt, DIA-NN report.parquet, and mzML/mzXML. Distinguishes Intensity (raw) vs LFQ intensity (MaxLFQ) vs iBAQ, treats a MaxQuant zero as missing (NaN, not log2(-inf)), and inherits the acquisition mode's missingness contract (DDA MNAR vs DIA MCAR). Use when starting an analysis from raw spectra or a search engine output. Downstream normalization and stats are differential-abundance; reporter-ion/MaxLFQ quant is quantification; protein grouping is protein-inference.

fridrichmethod Bundle

Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.

fridrichmethod Bundle

Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

fridrichmethod Bundle

Analyzes data-independent acquisition (DIA) proteomics by scoring reconstructed fragment-chromatogram peak groups against a decoy null with DIA-NN (library-free directDIA, library-based, or deep-learning predicted-library routes), Spectronaut, OpenSWATH, and EncyclopeDIA. Frames the deliverable around q-value LEVEL (precursor/peptide/protein-group) and CONTEXT (run vs experiment-wide/global) rather than a bare "1% FDR", and around the duty-cycle-vs-selectivity acquisition tradeoff (window design, staggered demultiplexing, diaPASEF, narrow-window Astral). Use when identifying and quantifying proteins from DIA mass spectrometry runs and filtering DIA-NN report.parquet/matrix output. Building the spectral library itself is spectral-libraries; normalization and protein roll-up is quantification; statistical testing of the matrix is differential-abundance.

26zl Bundle

This skill covers detecting cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) systems including man-in-the-middle attacks on industrial protocols, unauthorized command injection into PLCs, HMI compromise, historian data manipulation, and denial-of-service against control system communications. It leverages OT-specific intrusion detection systems, industrial protocol anomaly detection, and process data analytics to identify attacks that traditional IT security tools miss.

26zl Bundle

Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.

26zl Bundle

Performs User and Entity Behavior Analytics (UEBA) to detect anomalous user activities including impossible travel, unusual access patterns, privilege abuse, and insider threats using SIEM-based behavioral baselines and statistical analysis. Use when SOC teams need to identify compromised accounts or insider threats through deviation from established behavioral norms.