qsv

Build a Visual Data Dictionary — an interactive qsv viz smart dashboard (a Data Schematic) driven by an LLM-inferred JSON Schema data dictionary, with the dictionary browsable beside the charts. Use when the user asks for a visual data dictionary, a documented dashboard, a dictionary-driven dashboard or Data Schematic, or wants to explore and document a CSV at the same time. Optionally bins rows into GeoJSON regions.

Machine-readable journal format for reproducible data analysis operations

Performance guide covering index files, stats cache, and frequency cache accelerators for qsv

Infer a semantic ontology from all files in the working directory - entities, attributes, relationships, domain taxonomy, and cross-file join paths. Outputs ONTOLOGY.md.

Standard disclaimer and attribution templates for AI-generated or AI-assisted data analysis outputs

Create publication-quality visualizations from CSV/TSV/Excel data using Python

Validate data and analysis before sharing - methodology, accuracy, bias, and data quality checks

Quality dimensions quick reference and remediation decision tree for tabular data assessment

Profile a CSV/TSV/Excel file - detect format, compute statistics, show value distributions

Join two datasets with automatic strategy selection (joinp vs join vs sqlp)

Generate AI-powered Data Dictionary, Description, and Tags for a CSV/TSV/Excel file

Convert between CSV, TSV, Excel, JSONL, Parquet, and other tabular formats

Clean a CSV/TSV/Excel file - fix headers, trim whitespace, remove duplicates, validate

Standard workflow order, tool selection matrix, and composition patterns for qsv CSV data wrangling

Run SQL queries against CSV/TSV/Excel files using Polars SQL engine

Queries U.S. Bureau of Labor Statistics data using the BLS MCP server. Use when user asks about CPI, inflation, unemployment, employment, wages, jobs, labor statistics, producer prices, or any economic indicator tracked by BLS. Maps natural language to correct series IDs and tools. Copied from https://github.com/larasrinath/bls_mcp

Respond to all pending review comments on the current PR — fetch comments, apply fixes, verify accuracy, test, commit, and reply. Use when addressing Copilot reviews, GitHub PR reviews, or any batch of review feedback.

Prepare a qsv release by bumping versions across all files and updating changelog

Prepare an MCP server and plugin release by bumping versions across all files and updating changelog

Build an interactive HTML dashboard — a Data Schematic — with charts, filters, and tables. Use when creating a dashboard, an executive overview with KPI cards, turning query results into a shareable self-contained report, building a team monitoring snapshot, or needing multiple charts with filters in one browser-openable file.