Total 50,476 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Use when you want to search for or download experimentally-determined 3D structures for biomolecules (proteins, nucleic acids, bound ligands). Supports searching by sequence similarity, structure similarity, chemical and other attributes. Also use to get metadata about biomolecular structure experiments.
Guides quantitative research for markets and finance—research question framing, data sourcing and quality checks, descriptive and inferential statistics, time series and panel methods (high level), factor and signal research, backtest design and pitfalls (lookahead, survivorship), risk metrics (volatility, drawdown, Sharpe limitations), regime and stress analysis, and reproducible notebooks or reports with explicit limitations and uncertainty communication. Use when the user mentions "quantitative research", "quant researcher", "factor research", "signal backtest", "time series analysis", "panel regression", "alpha research", "Sharpe ratio analysis", "survivorship bias", "lookahead bias", "econometric analysis", or "risk factor model". Not for production ML pipelines (data-scientist, ml-research-engineer), equity narrative reports (equity-research skills), SOX accounting (financial-statements), legal investment advice, or trading execution systems (senior-software-engineer).
cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.
Plan a migration onto MotherDuck. Use when moving from Snowflake, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, validation, rollback, and native-versus-DuckLake posture.
Builds geocoding workflows in CARTO that convert street addresses or place names into geographic coordinates. Triggers when the user mentions geocoding, address to coordinates, address resolution, geolocate addresses, "add geometry from addresses", lat/lon from address, place name to point, address matching, forward geocoding, converting addresses to points, or has tabular data with address columns but no spatial geometry column and needs to create one.
Load data into MotherDuck from local files, object storage, HTTPS, dataframes, or external databases. Use when choosing a MotherDuck-specific ingestion path, especially CTAS and INSERT...SELECT, bulk loading, secrets, and Postgres-endpoint versus DuckDB-client tradeoffs.
This skill should be used when analyzing sector rotation patterns and market cycle positioning. It fetches sector uptrend data from CSV (no API key required) and optionally accepts chart images for supplementary analysis. Use this skill when the user requests sector rotation analysis, cyclical vs defensive assessment, overbought/oversold identification, or market cycle phase estimation. All analysis and output are conducted in English.
Used for header-only preflight of one DICOM series folder before conversion or inference. Not for de-identification or clinical clearance.
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.
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.