Agent Skills: CSV Wrangling with qsv

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.