Total 53,176 skills, Data Processing has 2676 skills
Showing 12 of 2676 skills
Provide US dividend tax and account-location workflow for Kanchi-style income portfolios. Use when users ask about qualified vs ordinary dividends, 1099-DIV interpretation, REIT/BDC distribution treatment, holding-period checks, or taxable-vs-IRA account placement decisions for dividend assets.
Comprehensive systems biology and pathway analysis using multiple pathway databases (Reactome, KEGG, WikiPathways, Pathway Commons, BioModels). Performs pathway enrichment, protein-pathway mapping, keyword searches, and systems-level analysis. Use when analyzing gene sets, exploring biological pathways, or investigating systems-level biology.
Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
Pinecone integration. Manage Indexs. Use when the user wants to interact with Pinecone data.
美股基础数据查询。美股列表、交易日历。 当用户询问"美股列表""美股交易日""美股代码查询"时触发。
Guide for querying DeFi flow data and events using DefiLlama MCP tools. Covers bridge flows, ETF inflows/outflows, stablecoin supply, institutional/DAT holdings with mNAV ratios, hacks and exploits, fundraising rounds, CEX volumes, open interest, and protocol treasuries. Use when users ask about bridge volume, ETF flows, stablecoin supply, MicroStrategy holdings, DeFi hacks, funding rounds, exchange volume, or treasury data.
Data visualization design based on Stanford CS448B. Use when: (1) Choosing appropriate chart types for data (2) Selecting visual encodings (position, color, size) (3) Critiquing or improving visualizations (4) Building D3.js visualizations (5) Designing interactions and animations (6) Choosing color palettes for accessibility (7) Visualizing networks or text data Covers Bertin, Mackinlay, Cleveland & McGill principles.
Visualizes datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction. Use when exploring dataset structure, finding clusters, identifying outliers, or understanding data distribution.
Patterns for building, maintaining, and scaling bioinformatics workflows. Covers Nextflow, Snakemake, WDL/Cromwell, container orchestration, and best practices for reproducible computational biology. Use when ", " mentioned.
Apply causal inference whenever the user is interpreting metrics, debugging system behavior, reading A/B test results, or trying to understand whether an observed change was caused by an action or by something else. Triggers on phrases like "X caused Y", "since we deployed this, metrics changed", "the A/B test showed a lift", "why did this metric move?", "is this correlation or causation?", "we changed X and Y improved", "how do we know this worked?", "the data shows…", or any situation where conclusions are being drawn from observational data. Also trigger before any decision based on metric interpretation — confusing correlation with causation leads to interventions that don't work and misattribution of credit. Never assume causation without applying this skill.
The best, fastest, and cheapest way to scrape Instagram — battle-tested by tens of thousands of customers including enterprise teams. Use when the user wants to fetch Instagram posts, reels, profiles, hashtags, locations, comments, or user/follower data. Five specialized actors cover every Instagram data surface.
Analyze spreadsheet data, generate insights, create visualizations, and build reports from Excel/CSV data.