Total 54,018 skills, Data Processing has 2767 skills
Showing 12 of 2767 skills
Parse and analyze STDF (Standard Test Data Format) semiconductor test files. Convert STDF to CSV/XLSX, generate analysis reports, correlation reports, PDF charts, and extract specific test data.
Track crypto holdings across exchanges. Calculate P&L, asset allocation, and generate performance reports.
Complete Guide to QMT (Xuntou High-Speed Strategy Trading System) Python Strategy Development. Covers strategy writing, backtesting, live trading, API references, and code examples. Use this skill when developing QMT quantitative strategies or querying QMT APIs.
Tongdaxin Quantitative Data Retrieval Skill. Use this skill when users mention tdxquant, Tongdaxin, TdxQuant, tqcenter, and need to obtain A-share market data (market snapshot, K-line, financial data, sector data, convertible bonds, new stocks, trading data, etc.), query trading calendars, execute Tongdaxin formulas, subscribe to market quotes, or place trading orders.
6 geoscience & climate skills. Trigger: earth science data, GIS, remote sensing, climate modeling. Design: geospatial tools, satellite data processing, and environmental models.
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.
Package specification compliance for Elastic integration packages. Covers manifest structure (format_version, conditions, variables, routing rules), changelog schema and semantic version bumps, and alignment with the upstream elastic/package-spec. Use when building or reviewing manifest.yml, changelog.yml, or debugging elastic-package lint/check errors on package metadata.
Pull Bigdata.com (RavenPack) financial and news data through the official `bigdata-client` SDK and its public `/v1/*` REST endpoints when the Bigdata MCP server returns only pre-synthesized tearsheets but you need the machine-readable substrate underneath. MCP search returns prose chunks (text + relevance only — no per-chunk sentiment, no entity spans); its tearsheets give only aggregate values, not computable time series or per-field JSON. This skill bundles a verified, cost-guarded toolkit over the official REST API: annotated chunk search, entity/ISIN resolution, analyst estimates, calendar/surprise/ ratings/targets, financial statements, TTM metrics & ratios, prices, dividends, revenue segments, a daily entity-sentiment series, co-mention graph, screener, and batch search. Use it whenever the user mentions Bigdata.com, RavenPack, a `bd_v2_` key, the bigdata MCP, rp_entity_id, chunk/query_unit cost, or wants structured financials, fundamentals, prices, sentiment, or annotated news.
Install cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.
Run OpenMMDL molecular dynamics workflows via the FastFold Workflows API (`openmmdl_v1`) from local topology + optional ligand files, prepare draft scripts, execute drafts, wait for completion, fetch artifacts/metrics, and extract trajectory frames. Use when users ask for OpenMMDL, protein-ligand MD, OpenMMDL script preparation, or `/openmmdl/results/<workflow_id>` reruns.
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.