Total 53,207 skills, Data Processing has 2692 skills
Showing 12 of 2692 skills
Provide comprehensive clinical interpretation of somatic mutations in cancer. Given a gene symbol + variant (e.g., EGFR L858R, BRAF V600E) and optional cancer type, performs multi-database analysis covering clinical evidence (CIViC), mutation prevalence (cBioPortal), therapeutic associations (OpenTargets, ChEMBL, FDA), resistance mechanisms, clinical trials, prognostic impact, and pathway context. Generates an evidence-graded markdown report with actionable recommendations for precision oncology. Use when oncologists, molecular tumor boards, or researchers ask about treatment options for specific cancer mutations, resistance mechanisms, or clinical trial matching.
Analyze spatial transcriptomics data to map gene expression in tissue architecture. Supports 10x Visium, MERFISH, seqFISH, Slide-seq, and imaging-based platforms. Performs spatial clustering, domain identification, cell-cell proximity analysis, spatial gene expression patterns, tissue architecture mapping, and integration with single-cell data. Use when analyzing spatial transcriptomics datasets, studying tissue organization, identifying spatial expression patterns, mapping cell-cell interactions in tissue context, characterizing tumor microenvironment spatial structure, or integrating spatial and single-cell RNA-seq data for comprehensive tissue analysis.
Detect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis. Analyze RSP/SPY concentration, yield curve, credit conditions, size factor, equity-bond relationship, and sector rotation to identify regime shifts between Concentration, Broadening, Contraction, Inflationary, and Transitional states. Run when user asks about macro regime, market regime change, structural rotation, or long-term market positioning.
Druckenmiller Strategy Synthesizer - Integrates 8 upstream skill outputs (Market Breadth, Uptrend Analysis, Market Top, Macro Regime, FTD Detector, VCP Screener, Theme Detector, CANSLIM Screener) into a unified conviction score (0-100), pattern classification, and allocation recommendation. Use when user asks about overall market conviction, portfolio positioning, asset allocation, strategy synthesis, or Druckenmiller-style analysis. Triggers on queries like "What is my conviction level?", "How should I position?", "Run the strategy synthesizer", "Druckenmiller analysis", "総合的な市場判断", "確信度スコア", "ポートフォリオ配分", "ドラッケンミラー分析".
You must use this when designing qualitative studies, developing coding schemes, or performing thematic analysis.
Inside Airbnb data warehouse built with Snowflake and dbt, demonstrating modern analytics engineering patterns with staging, intermediate, and mart layers.
조달청 나라장터 부정당제재업체정보를 공공데이터포털 API(k-skill-proxy 경유)로 조회한다. 사업자등록번호 정확 일치로 조회시점 현재 유효한 입찰참가자격 제한(부정당제재)의 기간·제재기관·근거법률을 확인한다.
This is used when users or Agents need to obtain, query, synchronize, analyze or export A-share market data, financial reports, valuations, indices, sectors, public funds, featured data or local DuckDB data via Hithink Finance Data Service, or need to select, install, configure, diagnose REST API, MCP, hithink-finance CLI, Python SDK/marketdb.
Build ETL pipelines and analytics dashboards for the Harvard Art Museums API using Python, SQL, and Streamlit
Compute market-data and trading analytics with the `fintech-algorithms` npm package — 324 zero-dependency TypeScript algorithms covering technical indicators (RSI, MACD, moving averages, Bollinger Bands, ATR, OBV, Stochastic), candlestick and chart patterns, market breadth, bar construction from tick data, OHLC validation and cleaning, corporate actions, index and benchmark construction, market microstructure, matching engines, execution and TCA, statistical time series, on-chain metrics and EPS analytics. Use when asked to analyse a price series, compute or explain an indicator, detect a candlestick or chart pattern, build bars from ticks, validate or clean market data, wire up a market-data provider, or when writing code that needs any of these calculations to be correct rather than approximated.
Analyze user retention by cohort. Use when: measuring customer retention; understanding lifecycle patterns; comparing acquisition cohorts; tracking engagement over time; identifying churn risks
Twelve Data integration. Manage data, records, and automate workflows. Use when the user wants to interact with Twelve Data data.