csv-wrangling

name	csv-wrangling
description	Standard workflow order, tool selection matrix, and composition patterns for qsv CSV data wrangling

CSV Wrangling with qsv

Standard Workflow Order

Always follow this sequence when processing CSV data:

0. Setup (Cowork) - If relative paths don't resolve, call mcp__qsv__qsv_get_working_dir and mcp__qsv__qsv_set_working_dir to sync
1. Index - index (enables fast random access for subsequent commands)
2. Discover - sniff (detect format, encoding, delimiter) -> headers -> count
3. Profile - stats --cardinality --stats-jsonl (creates cache used by smart commands)
4. Inspect - slice --len 5 (preview rows), frequency --frequency-jsonl (value distributions with cache for reuse)
5. Transform - select, sort, dedup, rename, replace, search, sqlp, etc.
6. Validate - validate (against JSON Schema), stats (verify results)
7. Export - tojsonl, table, mcp__qsv__qsv_to_parquet, to (xlsx/sqlite/postgres/ods/datapackage)
8. Document - describegpt --all (AI-generated Data Dictionary, Description & Tags)

Tool Selection Matrix

Task	Best Tool	Alternative	When to Use Alternative
Select columns	select	sqlp	Need computed columns
Filter rows	search	sqlp	Complex WHERE conditions
Sort data	sort	sqlp	Need ORDER BY with LIMIT
Remove duplicates	dedup	sqlp	Need GROUP BY dedup
Join two files	joinp	join	join for memory-constrained
Aggregate/GROUP BY	sqlp	frequency	frequency for simple counts; --frequency-jsonl creates cache
Column stats	stats	moarstats	moarstats for extended stats
Find/replace	replace	sqlp	sqlp for conditional replace
Reshape wide->long	transpose --long	-	DuckDB UNPIVOT (external) for complex reshaping
Reshape long->wide	pivotp	sqlp	Complex pivots
Concatenate files	cat rows	cat rowskey	Different column orders
Sample rows	sample	slice	slice for positional ranges
Document dataset	describegpt	—	AI-generated Data Dictionary, Description & Tags

qsv Selection Syntax

Used by select, search, sort, dedup, frequency, and other commands:

Syntax	Meaning	Example
name	Column by name	select "City"
1	Column by 1-based index	select 1
1,3,5	Multiple columns	select 1,3,5
1-5	Range (inclusive)	select 1-5
!col	Exclude column	select '!SSN'
!1-3	Exclude range	select '!1-3'
/regex/	Match column names	select '/^price/'

Common Pipeline Patterns

Clean and Deduplicate

sniff -> index -> safenames -> fixlengths -> sqlp (TRIM) -> dedup -> validate

Profile and Analyze

sniff -> index -> stats --cardinality --stats-jsonl -> read .stats.csv -> frequency (on key columns) -> sqlp (GROUP BY queries)

Before writing SQL: read .stats.csv to learn column types, cardinality, nullcount, min/max, sort order. Run frequency on columns you'll GROUP BY or filter on. Use this to write precise WHERE clauses, correct type casts, and avoid unnecessary COALESCE.

For repeated SQL queries on large CSV (> 10MB), consider converting to Parquet: sniff -> index -> stats -> to_parquet -> sqlp (using read_parquet()). Note: sqlp can query CSV of any size directly.

Join and Enrich

index (both files) -> stats (both) -> joinp -> select (keep needed columns) -> sort

Profile and Document

sniff -> index -> stats --cardinality --stats-jsonl -> describegpt --all

Convert and Export

excel (to CSV) -> index -> stats -> select -> tojsonl / qsv_to_parquet

Batch Convert to Multiple Formats

excel (to CSV) -> index -> stats -> to xlsx report.xlsx
excel (to CSV) -> index -> stats -> to sqlite report.db
excel (to CSV) -> index -> stats -> to parquet parquet_output_dir

File Integrity Verification

blake3 file.csv > checksums.b3 (before transfer) -> blake3 --check checksums.b3 (after transfer)

Delimiter Handling

• CSV (,): default, no flag needed
• TSV (\t): use --delimiter '\t' or file extension .tsv
• SSV (;): use --delimiter ';' or file extension .ssv
• Auto-detect: set QSV_SNIFF_DELIMITER=1 environment variable

Important Notes

• Column indices are 1-based, not 0-based
• --no-headers flag changes behavior significantly - most commands assume headers exist
• Output goes to stdout by default; use --output file.csv to write to file
• Many commands auto-detect .sz (Snappy compressed) files transparently
• cat rows requires same column order; use cat rowskey for different schemas
• dedup loads all data into memory and sorts internally; use --sorted flag if input is already sorted to enable streaming mode with constant memory
• sort loads entire file into memory; for huge files use sqlp with ORDER BY
• For repeated SQL queries on large CSV (> 10MB), consider converting to Parquet with mcp__qsv__qsv_to_parquet for faster performance. Parquet works ONLY with sqlp and DuckDB — all other qsv commands need CSV/TSV/SSV input

Tool Discovery

Use mcp__qsv__qsv_search_tools to discover commands beyond the initially loaded core tools. There are 55 qsv skill-based commands covering selection, filtering, transformation, aggregation, joining, validation, formatting, conversion, and more.

Operational Notes

• Timeout: Default operation timeout is 10 minutes (configurable via QSV_MCP_OPERATION_TIMEOUT_MS, max 30 min). Allow operations to run to completion.
• Memory: dedup, sort, reverse, table, transpose, pragmastat, and stats (with extended stats) load entire files into memory. For files >1GB, prefer extdedup/extsort via mcp__qsv__qsv_command.
• Cowork path architecture: qsv runs on the HOST machine. File paths must be valid on the host. Always verify with mcp__qsv__qsv_get_working_dir.
• Sequential operations: Prefer sequential over parallel qsv calls to avoid queuing delays: index → stats → analysis.
• Large files (>5GB): Let mcp__qsv__qsv_frequency run to completion. Only fall back to mcp__qsv__qsv_sqlp with GROUP BY if the server timeout is exceeded.
• Context window: Save outputs to files rather than returning to chat. Use mcp__qsv__qsv_slice or mcp__qsv__qsv_sqlp with LIMIT to inspect subsets.