Install
$ agentstack add skill-krastanov-juliallmagentskills-julia-csv ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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; supportsreusebuffer=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,footerskipselect/drop,limittypes,typemap,stringtype,pool,downcaststrict,silencewarnings,maxwarnings
Delimiter and cell interpretation:
delim,ignorerepeated(fixed-width style data)missingstringquoted,quotechar/openquotechar/closequotechar,escapechardateformat,decimal,groupmark,truestrings,falsestrings
Threading and chunking:
ntasks,rows_to_check
Support Multiple Input Forms
CSV.jl supports:
- filename/filepath
IO/CmdVector{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,escapecharmissingstring,dateformat,decimalappend,header,newline,quotestringstransform,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.
- Author: Krastanov
- Source: Krastanov/JuliaLLMAgentSkills
- License: Unlicense
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.