Total 54,409 skills, Data Processing has 2786 skills
Showing 12 of 2786 skills
Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit
End-to-end ETL pipeline and analytics application for Harvard Art Museums API with Streamlit dashboards
Update financial models with new data — quarterly earnings, management guidance, macro changes, or revised assumptions. Adjusts estimates, recalculates valuation, and flags material changes. Use after earnings, guidance updates, or when assumptions need refreshing. Triggers on "update model", "plug earnings", "refresh estimates", "update numbers for [company]", "new guidance", or "revise estimates".
Use when large data ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, or table synchronization needs to become much faster while preserving data correctness.
Match external CSV/JSONL records to CRM contacts (by email) or companies (by domain) and write enriched data back in one pass using `hubspot objects upsert`.
10 data wrangling skills. Trigger: messy data, format conversion, missing values, data reshaping. Design: pipeline-oriented recipes for common data cleaning and transformation tasks.
Read and write large cuPyNumeric arrays to HDF5 with Legate's parallel, distributed HDF5 I/O (legate.io.hdf5: to_file, from_file, from_file_batched). Use when a developer needs to save a cuPyNumeric array to an .h5/.hdf5 file, load an HDF5 dataset into a distributed cuPyNumeric array, read a large HDF5 dataset in chunks, hand arrays to an HPC pipeline as a single file, or accelerate HDF5 disk I/O with GPUDirect Storage (GDS). Do not use it for Parquet/cuDF/raw-binary or other sharded/custom layouts (see the cupynumeric-parallel-data-load skill), Zarr or object-store/S3 output, .npz or pickled archives, plain h5py without cuPyNumeric, or pure array compute such as FFT, matmul, or reductions.
Fetches cryptocurrency market data, prices, technical analysis, news, and trends using the CoinMarketCap MCP. Use for ANY question involving cryptocurrencies, tokens, or blockchain markets, even if the user doesn't explicitly ask for data. This includes price checks, portfolio questions, market analysis, coin comparisons, holder metrics, technical indicators, and news. Trigger: "bitcoin", "ETH", "crypto", "token price", "market cap", "how is [coin] doing", "/cmc-mcp"
Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs. Use this skill when the user needs to calculate expected return on an asset, interpret beta as systematic risk exposure, evaluate whether an investment compensates for risk, or when they ask 'what return should I expect', 'what is the risk premium', or 'how does beta affect pricing'.
Describes how blockchain analytics platforms work in practice, typical use cases (markets, compliance, law enforcement, tax, market integrity), tool layers like visualizers and tracers, and limitations of heuristic attribution. Use when the user asks about blockchain analytics for AML, transaction monitoring, forensic tracing, institutional ops, or taint-style analysis at a high level.
Read, edit, analyze, and create Microsoft Excel files (.xlsx, .xls, .xlsm, .csv, .tsv). Use when a user asks to: (1) Open/read/inspect an Excel file, (2) Edit or modify spreadsheet data, formulas, or formatting, (3) Analyze spreadsheet data and provide insights, statistics, or trends, (4) Create new Excel files with data, formulas, charts, or formatting, (5) Convert between CSV/TSV and Excel formats, (6) Build financial models or dashboards in Excel.
Fetch web page content via Chrome DevTools Protocol (CDP). Full JS rendering, handles redirects (including Google News). Use when you need to read the text content of a web page, scrape articles, or extract information from URLs. Zero dependencies — Python 3 stdlib only. Cross-platform (Mac, Windows, Linux).