Data & Analytics

oimiragieo Bundle

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

oimiragieo Bundle

Focuses on pandas-specific rules for data manipulation, including method chaining, data selection using loc/iloc, and groupby operations.

oimiragieo Bundle

Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.

oimiragieo Bundle

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

gptomics 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).

g1joshi Skill

SQL database queries, joins, aggregations, subqueries, and optimization. Use for .sql files and database operations.

aradotso-trending-skills Skill

Terminal-based spreadsheet TUI tool written in Go for editing CSV files in the terminal with vim-like keybindings

oimiragieo 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.

aradotso-trending-skills Skill

Local token cost analytics dashboard for Claude Code sessions — reads JSONL transcripts and provides per-prompt cost breakdowns, heatmaps, and usage insights.

gptomics Bundle

Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.

oimiragieo Bundle

Excel spreadsheet generation using openpyxl and xlsxwriter. Convert Claude output, data analysis results, and tabular data into formatted .xlsx files with charts, formulas, conditional formatting, and multiple sheets. Use for generating Excel reports programmatically.

g1joshi Skill

DuckDB analytical database for OLAP workloads. Use for embedded analytics.

g1joshi Skill

Oracle Database with PL/SQL, RAC, and enterprise features. Use for enterprise systems.

g1joshi Skill

Google BigQuery for analytics, ML, and data warehousing. Use for large-scale analytics.

g1joshi Skill

Drizzle TypeScript ORM with SQL-like syntax. Use for database access.

g1joshi Skill

ClickHouse columnar database for analytics. Use for real-time analytics.

g1joshi Skill

PostgreSQL relational database with JSONB, CTEs, window functions, and extensions. Use for SQL queries.

g1joshi Skill

CockroachDB distributed SQL database. Use for geo-distributed data.

oimiragieo Bundle

Convert natural language queries to SQL. Use for database queries, data analysis, and reporting.

g1joshi Skill

Elasticsearch search and analytics engine with full-text search. Use for search and logging.

boshi-xixixi Bundle

当用户需要进行数据库设计、SQL 优化或数据库迁移时使用。此 Skill 专注于提供数据库架构设计、性能调优和数据迁移策略的专业指导。

ariffazil Skill

In-process columnar SQL analytics engine over Parquet, CSV, JSONL, and well log (.las) files using DuckDB and Polars.

oimiragieo Bundle

Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.

oimiragieo Bundle

Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.

tibsfox Skill

Emit Postgres.sql and SQLite.sql from a single schema spec so tools work across both drivers without duplicating DDL by hand. Use when designing a schema that needs to support both shared Postgres deployments and zero-config SQLite. Reduces two-file sync burden to a single source edit.

oimiragieo Bundle

Database expert including Prisma, Supabase, SQL, and NoSQL patterns

crestapps Skill

Skill for configuring the Orchard Core data layer. Covers database provider selection, YesSql configuration options, connection string setup, DbConnection access, SQL dialect abstraction, table prefix conventions, Dapper integration, and data access patterns. Use this skill when requests mention Orchard Core Data, Configure and Use the Orchard Core Data Layer, Sqlite Connection Pooling Configuration, YesSql Configuration Options, Database Table Options, Creating a DbConnection, or closely related Orchard Core implementation, setup, extension, or troubleshooting work. Strong matches include work with OrchardCore.Data, OrchardCore.Environment.Shell, IDbConnectionAccessor, IStore, ISqlDialect, ShellSettings, IServiceCollection, MyDataService, IEnumerable, OrderService, INSERT, INTO. It also helps with Database Table Options, Creating a DbConnection, SQL Dialect Abstraction, plus the code patterns, admin flows, recipe steps, and referenced examples captured in this skill.

gptomics 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.

gptomics 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.

gptomics 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.

gptomics 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.

gptomics 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.

sboghossian-mini-claude-for-legal Skill

Use when asked to draft a Cookie Policy for a website or application. Covers all five cookie categories (strictly necessary, functional, analytics, advertising, social media), the required consent mechanism and granular opt-in standards under EU ePrivacy/GDPR, California CCPA opt-out requirements, and notes for UAE, KSA, and Lebanon. Should be paired with a Privacy Policy and Terms of Service.

sboghossian-mini-claude-for-legal Skill

Use when a user asks what has changed in the platform recently, wants to understand new features, or an administrator needs to communicate recent updates to their team. This is a platform documentation skill covering the in-app changelog reader — how updates are categorized, surfaced, and filtered by user cohort (admins see admin changes; lawyers see lawyer-relevant changes).

gptomics 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.

gptomics 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.