Total 50,523 skills, Data Processing has 2561 skills
Showing 12 of 2561 skills
Splunk integration. Manage data, records, and automate workflows. Use when the user wants to interact with Splunk data.
Salesforce Data Cloud Prepare phase. Use this skill when the user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations. TRIGGER when: user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations, or asks about ingestion into Data Cloud. DO NOT TRIGGER when: the task is connection setup only (use connecting-datacloud), DMOs and identity resolution (use harmonizing-datacloud), or query/search work (use retrieving-datacloud).
Comprehensive token analysis combining price, market cap, unlock schedule, DeFi deposits, and yield opportunities. Use when the user asks to analyze a token, research a token, check token fundamentals, or wants full token intelligence including vesting and DeFi usage.
Core reference for DefiLlama MCP tools. Maps DeFi questions to the correct tool call with proper parameters. Covers entity conventions, metric interpretation, stock vs flow distinctions, percentage formatting, and error recovery. Use whenever querying DeFi data — protocol TVL, token prices, chain metrics, fees, revenue, yields, stablecoins, bridges, ETFs, hacks, raises, treasuries, or institutional holdings.
Full blockchain ecosystem analysis covering TVL, top protocols, bridge flows, stablecoin supply, DEX volume, fees, and user activity on a chain. Use when the user asks about a blockchain's ecosystem, "what's happening on Solana", chain health, or a chain-level overview.
Find substitute materials using CWICR data. Identify equivalent alternatives based on function, cost, and availability.
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.
Macrometa integration. Manage data, records, and automate workflows. Use when the user wants to interact with Macrometa data.
Read, write, and manipulate SEG-Y seismic data files. Fast C library with Python bindings for trace, header, inline, and crossline access. Use when Claude needs to: (1) Read/inspect SEG-Y files, (2) Extract trace data or headers, (3) Access 3D survey data by inline/crossline, (4) Create new SEG-Y files from arrays, (5) Modify existing SEG-Y files, (6) Extract subsets of seismic data, (7) Read/write Seismic Unix format.
N-dimensional labeled arrays for geoscience data. Read/write NetCDF, work with climate and oceanographic datasets, perform multi-dimensional analysis with labeled coordinates. Use when Claude needs to: (1) Read/write NetCDF or Zarr files, (2) Work with multidimensional arrays with labeled dimensions, (3) Analyze climate, ocean, or atmosphere data, (4) Compute temporal aggregations (daily/monthly/annual means), (5) Perform area-weighted statistics, (6) Process large datasets with Dask, (7) Apply CF conventions to scientific data.
Groundwater time series analysis and modelling using transfer function noise models. Use when Claude needs to: (1) Analyze groundwater level time series, (2) Model well responses to precipitation/pumping, (3) Calibrate aquifer parameters from head data, (4) Forecast or hindcast groundwater levels, (5) Decompose hydrological signals into components, (6) Compare response functions, (7) Perform model diagnostics and uncertainty analysis.
Validate the column contract of a newly written table — column set, types, and nullability match expectations. Object existence and row counts are handled by the builtin layer and are out of scope. Data-content assertions belong to project-level validator skills.