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Found 91 Skills
Comprehensive guide for NumPy - the fundamental package for scientific computing in Python. Use for array operations, linear algebra, random number generation, Fourier transforms, mathematical functions, and high-performance numerical computing. Foundation for SciPy, pandas, scikit-learn, and all scientific Python.
Modern Python coaching covering language foundations through advanced production patterns. Use when asked to "write Python code", "explain Python concepts", "set up a Python project", "configure Poetry or PDM", "write pytest tests", "create a FastAPI endpoint", "run uvicorn server", "configure alembic migrations", "set up logging", "process data with pandas", or "debug Python errors". Triggers on "Python best practices", "type hints", "async Python", "packaging", "virtual environments", "Pydantic validation", "dependency injection", "SQLAlchemy models".
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids), vector/similarity search, GPUDirect Storage file IO, interactive dashboards, geospatial analysis, medical imaging, and sparse eigensolvers. Also use when you see CPU-bound Python code (loops, large arrays, ML pipelines, graph analytics, image processing) that would benefit from GPU acceleration, even if not explicitly requested.
A retrieval and Q&A assistant for local knowledge base directories. Core processes: (1) Hierarchical index navigation (2) When encountering PDF/Excel files, must first read references to learn processing methods (3) Retrieve after processing files. Use a combination of grep, Read, pdfplumber, pandas for progressive retrieval based on file types, avoiding full-file loading. Used when user questions involve "answering questions/retrieving information/searching for materials from knowledge base directories".
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Spreadsheet toolkit (.xlsx/.csv). Create/edit with formulas/formatting, analyze data, visualization, recalculate formulas, for spreadsheet processing and analysis.
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Handle spreadsheet operations (Excel/CSV) with high-fidelity modeling, financial analysis, and visual verification. Use for budget models, data dashboards, and complex formula-heavy sheets. Use proactively when zero formula errors and professional standards are required. Examples: - user: "Build an LBO model" -> create Excel with banking-standard formatting - user: "Analyze this data and create a dashboard" -> use openpyxl + artifact_tool - user: "Verify formulas in this spreadsheet" -> run recalc.py to check for errors
Working with Excel files programmatically.
Excel spreadsheet toolkit for creating, reading, and manipulating .xlsx files. Supports formulas, formatting, charts, and financial modeling with industry-standard conventions. Use for data analysis, financial models, reports, and spreadsheet automation.
Act as a Renaissance Tech-level quantitative systems engineer. Build unified feature engines instead of isolated strategies, rigorously test predictive variables, and assemble scoring models.