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SKILL verified Unlicense Self-run

Julia Csv

skill-krastanov-juliallmagentskills-julia-csv · by Krastanov

Parse and write delimited text data with CSV.jl, including CSV.File/CSV.read/CSV.Rows/CSV.Chunks for ingestion and CSV.write/CSV.RowWriter for output, with control over schema, delimiters, missing values, quoting, dates, pooling, and multithreading. Use this skill when handling CSV/TSV/fixed-width style files in Julia.

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Install

$ agentstack add skill-krastanov-juliallmagentskills-julia-csv

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

Julia Csv

Use CSV.jl for high-performance reading/writing of delimited data with explicit control over parsing behavior.

Choose the Read Mode

  • CSV.File(input; kw...): full-column parse, table-like object, best default.
  • CSV.read(input, sink; kw...): parse and hand columns directly to sink (e.g. DataFrame) without intermediate copies.
  • CSV.Rows(input; kw...): row-streaming with lower memory footprint; supports reusebuffer=true.
  • CSV.Chunks(input; ntasks=...): chunked parsing for very large files.
using CSV, DataFrames

f = CSV.File("data.csv")
df = CSV.read("data.csv", DataFrame)

Parse Schema and Layout Explicitly

Common schema/layout controls:

  • header, normalizenames, skipto, footerskip
  • select / drop, limit
  • types, typemap, stringtype, pool, downcast
  • strict, silencewarnings, maxwarnings

Delimiter and cell interpretation:

  • delim, ignorerepeated (fixed-width style data)
  • missingstring
  • quoted, quotechar / openquotechar / closequotechar, escapechar
  • dateformat, decimal, groupmark, truestrings, falsestrings

Threading and chunking:

  • ntasks, rows_to_check

Support Multiple Input Forms

CSV.jl supports:

  • filename/filepath
  • IO / Cmd
  • Vector{UInt8}
  • vector of inputs for vertical concatenation (matching schema)

Gzip (.gz) input is handled automatically; use buffer_in_memory=true if needed.

Write CSV Output

CSV.write("out.csv", table)
rows = CSV.RowWriter(table)

Useful write controls:

  • delim, quotechar, openquotechar, closequotechar, escapechar
  • missingstring, dateformat, decimal
  • append, header, newline, quotestrings
  • transform, bom, compress, partition, bufsize

Reference

  • references/csv-patterns.md - read/write mode selection and option patterns

Related Skills

  • julia-prettytables - rendering parsed tabular data

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.