Total 50,395 skills, Data Processing has 2557 skills
Showing 12 of 2557 skills
Embed hierarchical data in hyperbolic space via npx ruvector Poincare ball model, compute geodesic distances
Discover and inspect Omni Analytics models, topics, views, fields, dimensions, measures, and relationships using the Omni CLI. Use this skill whenever someone wants to understand what data is available in Omni, explore their semantic model, find specific fields or views, check how tables join together, see what topics exist, or asks any variant of "what can I query", "what fields are available", "show me the model", "what data do we have", or "how is this data modeled". Also use when you need to understand the Omni model structure before building or modifying anything.
Run queries against Omni Analytics' semantic layer using the Omni CLI, interpret results, and chain queries for multi-step analysis. Use this skill whenever someone wants to query data through Omni, run a report, get metrics, pull numbers, analyze data, ask "how many", "what's the trend", "show me the data", retrieve dashboard query results, or perform any data retrieval through Omni's query engine. Also use when someone wants to programmatically extract data from an existing Omni dashboard or workbook.
Generate reproducible analysis artifacts — SQL queries, Python visualizations, and summary tables — as you work through a BigQuery data analysis. Use when asked to conduct a deep dive, exploratory analysis, or investigation that goes beyond a simple data lookup.
DTC Data Dashboard & Health Check Engine — Full-link data analysis, KPI tracking, industry benchmarking, data health assessment, market trend monitoring. Use when user mentions: data health check, data audit, KPI, dashboard, metrics tracking, metrics, baseline, benchmark, data analysis, revenue report, channel data, advertising data, ROAS tracking.
Use SNOMED CT's semantic attribute relationships to answer clinical questions. Finds concepts by relationship attribute (finding site, causative agent, associated morphology, procedure site), navigates the IS-A hierarchy, and composes property-filtered ValueSets. Use when the user asks things like "all disorders of the heart", "all procedures on the kidney", "all conditions caused by bacteria", "subtypes of hypertension", "symptoms of X", "complications of X", or any query that involves clinical relationships between concepts rather than simple text search.
Build effective charts, dashboards, and reports across analytics, infrastructure monitoring, and ML domains. Use for library selection, visualization UX, accessibility, and domain-specific dashboard design.
Discounted cash flow valuation and intrinsic value analysis for public companies. Use when the brief asks for DCF, fair value, intrinsic value, price target, undervalued or overvalued analysis, or "what is this company worth?"
End-to-end epidemiological data analysis — from research question to statistical report. Covers study design assessment, dataset discovery and download, data wrangling, confounder adjustment, regression modeling, sensitivity analysis, visualization, and biological interpretation. Integrates ToolUniverse tools for dataset discovery, literature search, and biological context with Python code execution for data analysis. Use whenever users ask to analyze health data, study disease risk factors, assess exposure-outcome relationships, or conduct observational epidemiology. Also use when users want to run regression on clinical/survey data, calculate odds ratios or hazard ratios from a dataset, adjust for confounders, or produce a Table 1. If the task involves downloading a health dataset and running statistical analysis on it, this is the right skill.
Investigate transcription factor binding, cis-regulatory elements, chromatin accessibility, and regulatory variant annotation. Use when asked about TF binding sites, enhancers, promoters, ChIP-seq data, ATAC-seq signals, candidate cis-regulatory elements (cCREs), or the regulatory impact of genomic variants.
Assess chemical and drug toxicity via adverse outcome pathways, real-world adverse event signals, and toxicogenomic evidence. Integrates AOPWiki (AOPWiki_list_aops, AOPWiki_get_aop) for mechanism- level pathway tracing, FAERS for post-market adverse event quantification, OpenFDA for label mining, and CTD for chemical-gene-disease evidence. Produces structured toxicity reports with evidence grading (T1-T4). Use when asked about toxicity mechanisms, adverse outcome pathways, AOP mapping, FAERS signal detection, or chemical-disease relationships for drugs or environmental chemicals.
Connect GWAS variants to biological pathways for drug target discovery. Maps disease-associated SNPs to causal genes via eQTL colocalization (GTEx), links genes to enriched pathways (Reactome, KEGG, MetaCyc), and identifies druggable targets within disease-relevant pathways. Use when asked to translate GWAS findings into mechanistic insights, find pathways enriched for disease genes, discover drug targets from genetic evidence, or answer questions like "What pathways are disrupted in type 2 diabetes based on GWAS data?"