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Found 18 Skills
Fast in-process analytical database for SQL queries on DataFrames, CSV, Parquet, JSON files, and more. Use when user wants to perform SQL analytics on data files or Python DataFrames (pandas, Polars), run complex aggregations, joins, or window functions, or query external data sources without loading into memory. Best for analytical workloads, OLAP queries, and data exploration.
Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
Exploratory Data Analysis skill for CSV and parquet datasets with deterministic profiling, drift/anomaly scans, contract generation and validation, and optional memory writeback into skill-system-memory. The implementation is Polars-first (lazy scan for large files and early `--sample` head), includes high-cardinality guards for profile/importance/contract flows, and supports categorical correlation with Cramer's V. Use when building or reviewing tabular fraud/risk/data-quality workflows, profiling new datasets, checking leakage or drift, or saving/validating data contracts.
Convert files between 140+ formats using the ConversionTools MCP server. Use when the user needs to convert documents (Word, PDF, Excel, PowerPoint), data formats (JSON, CSV, XML, YAML, Parquet), images (PNG, JPG, WebP, AVIF, HEIC, JXL, SVG), audio (MP3, WAV, FLAC), video (MOV, MKV, AVI to MP4), e-books (EPUB, MOBI, AZW), OCR text extraction, AI-powered data extraction, AI text-to-speech (TTS), AI speech-to-text transcription (STT), subtitle conversion (SRT, VTT, ASS), or website screenshots.
Use when writing or running Nushell commands, scripts, or pipelines - via the Nushell MCP server (mcp__nushell__evaluate), via Bash (nu -c), or in .nu script files. Also use when working with structured data (JSON, YAML, TOML, CSV, Parquet, SQLite), doing ad-hoc data analysis or exploration, or when the user's shell is Nushell.
Extract raw price dataframe for a test case