Total 55,062 skills, Data Processing has 2818 skills
Showing 12 of 2818 skills
You must use this when merging findings from multiple studies into a coherent narrative with grounded evidence.
Develops and troubleshoots dbt incremental models. Use when working with incremental materialization for: (1) Creating new incremental models (choosing strategy, unique_key, partition) (2) Task mentions "incremental", "append", "merge", "upsert", or "late arriving data" (3) Troubleshooting incremental failures (merge errors, partition pruning, schema drift) (4) Optimizing incremental performance or deciding table vs incremental Guides through strategy selection, handles common incremental gotchas.
Retrieve historical financial ratings and key metric scores over time using Octagon MCP. Use when analyzing overall ratings, return on assets, return on equity, discounted cash flow scores, debt-to-equity scores, and letter grades (A+, A, B, etc.) for any public company.
Use when analyzing FileMaker DDR to extract calculations, custom functions, and business logic for PostgreSQL import processes or maintenance scripts - focuses on understanding and adapting FileMaker logic rather than direct schema migration
Complete guide for Apache Spark data processing including RDDs, DataFrames, Spark SQL, streaming, MLlib, and production deployment
Best practices for NumPy array programming, numerical computing, and performance optimization in Python
Retrieve detailed balance sheet statement data including Total Assets, Current Assets, Non-Current Assets, Liabilities, Equity, and Net Debt for public companies. Use when analyzing financial position, capital structure, or leverage metrics.
Analyze 10-Q quarterly filings for public companies using Octagon MCP. Use when extracting quarterly performance metrics, revenue breakdown, operating margins, segment performance, and interim financial updates from SEC 10-Q filings.
Use to define schemas, topic tags, and lineage metadata for enriched signals.
Gravity and magnetic data processing and forward modelling using Fatiando a Terra. Use when Claude needs to: (1) Compute gravity forward models (point masses, prisms, tesseroids), (2) Apply terrain/Bouguer corrections, (3) Grid scattered potential field data with equivalent sources, (4) Perform upward/downward continuation, (5) Calculate magnetic anomalies from magnetized bodies, (6) Apply derivative filters (gradients, tilt angle), (7) Process regional or local gravity surveys.
Spatial data processing for geological modelling with GemPy. Use when Claude needs to: (1) Prepare spatial data for GemPy models, (2) Extract interface points from geological maps, (3) Process orientations/dip measurements, (4) Sample DEMs along profiles or cross-sections, (5) Convert between GIS formats and GemPy inputs, (6) Clip/transform vector/raster data for modeling, (7) Create model extents from geospatial bounds.
Read and parse DLIS (Digital Log Interchange Standard) and LIS (Log Information Standard) well log files. Use when Claude needs to: (1) Read/parse DLIS or LIS files, (2) Extract well log curves as numpy arrays, (3) Access file metadata and origin information, (4) Handle multi-frame or multi-file DLIS, (5) Convert DLIS to LAS or DataFrame, (6) Work with RP66 format well logs, (7) Process array or image log data.