Total 50,483 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Datos de Google Finance via batchexecute (API RPC interna sin auth ni API key). Quote, OHLC intraday 1-min y 5-min, OHLC daily, financials masivos (income/balance/cashflow), earnings, analyst recommendations + opinions, descripcion empresa, peers, news, indices globales (Dow/S&P/NASDAQ/VIX/DAX), sectors heatmap. Cobertura mercados US (NASDAQ/NYSE) y argentinos (BCBA). ⚠️ API NO oficial — leer LIMITATIONS_TROUBLESHOOTING.md antes de uso productivo.
Scraper de MarketWatch: quotes, financials (income/balance/cash flow), SEC filings, analyst estimates, options chain, historical OHLCV. Sin API key.
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.
Run molecular dynamics (MD) simulations via the FastFold Workflows API. Today supports the CALVADOS+OpenMM workflow (calvados_openmm_v1) from either an existing fold job (AF structure + PAE auto-resolved) or manual PDB+PAE upload, then waits for completion, fetches metrics/plots/CSV artifacts, and extracts trajectory frames as PDB files. Use when running an MD simulation with FastFold, CALVADOS + OpenMM, reading MD metrics/plots, extracting frames, or scripting submit → wait → results for an MD run.
Write and run AQL (Analytic Query Language) queries to answer data questions. Use this whenever the user asks for data, wants to query a dataset, needs to filter/aggregate/join data, or asks about metrics and dimensions in Holistics.
This skill should be used when the user asks to "use marimo", "create a marimo notebook", "debug a marimo notebook", "inspect cells", "understand reactive execution", "fix marimo errors", "convert from jupyter to marimo", or works with marimo reactive Python notebooks.
Apache Superset integration. Manage data, records, and automate workflows. Use when the user wants to interact with Apache Superset data.
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"
Column integration. Manage data, records, and automate workflows. Use when the user wants to interact with Column data.
Design effective data dashboards with proper KPI hierarchy, chart type selection, and interactive features. Use this skill when the user needs to create a dashboard, choose the right visualizations, organize metrics for different audiences, or evaluate dashboard tools — even if they say 'build a dashboard', 'our reports are confusing', 'which chart should I use', or 'executives can't find the metrics they need'.
Apply causal inference methods — counterfactual framework, instrumental variables, propensity score matching, and difference-in-differences — to estimate causal effects from observational data. Use this skill when the user needs to determine if X caused Y from non-experimental data, evaluate program/policy impact without a randomized trial, or control for confounders — even if they say 'did this change cause the improvement', 'how do we measure the impact without an experiment', or 'is this correlation or causation'.
Conduct Exploratory Data Analysis (EDA) using descriptive statistics, visualizations, and data quality checks. Use this skill when the user has a dataset and needs to understand its structure, find patterns, detect anomalies, or prepare data for further analysis — even if they say 'what does this data look like', 'find interesting patterns', 'clean this data', or 'summarize this dataset'.