JN Data Pipeline Tool

JN is a command-line ETL tool that uses NDJSON (newline-delimited JSON) as a universal data format. Chain commands with Unix pipes to build data pipelines.

Core Concept

All JN commands communicate via NDJSON:

{"name": "Alice", "age": 30}
{"name": "Bob", "age": 25}

One JSON object per line = streamable, memory-efficient data processing.

Four Essential Commands

1. jn cat - Read Data

Read any data source, output NDJSON:

# Basic files
jn cat data.csv                     # CSV → NDJSON
jn cat data.json                    # JSON → NDJSON
jn cat data.xlsx                    # Excel → NDJSON
jn cat data.yaml                    # YAML → NDJSON

# Force specific format
jn cat data.txt~csv                 # Treat .txt as CSV
jn cat data.log~json                # Treat .log as JSON

# Format with parameters
jn cat "data.csv~csv?delimiter=;"   # Semicolon-delimited
jn cat "data.csv?limit=100"         # Only read first 100 rows

# Read from stdin
cat data.csv | jn cat "-~csv"       # Pipe stdin as CSV

Filtering at read time:

# Filter during cat (applied AFTER reading)
jn cat "data.csv?city=NYC"              # Filter: city equals NYC
jn cat "data.csv?city=NYC&city=LA"      # OR logic: NYC or LA
jn cat "data.csv?city=NYC&age>25"       # AND logic: NYC and age>25
jn cat "data.csv?limit=100&city=NYC"    # Config + filter

2. jn filter - Transform Data

Filter/transform NDJSON using jq expressions:

# Simple filters
jn cat data.csv | jn filter '.age > 25'
jn cat data.csv | jn filter '.status == "active"'
jn cat data.csv | jn filter '.price < 100'

# Select specific fields
jn cat data.csv | jn filter '{name, email}'
jn cat data.csv | jn filter '{name, age, city: .location.city}'

# Transform values
jn cat data.csv | jn filter '.price = .price * 1.1'
jn cat data.csv | jn filter '.name = .name | ascii_upcase'

# Combine conditions (AND)
jn cat data.csv | jn filter '.age > 25 and .city == "NYC"'

# Combine conditions (OR)
jn cat data.csv | jn filter '.city == "NYC" or .city == "LA"'

# Select records
jn cat data.csv | jn filter 'select(.active)'
jn cat data.csv | jn filter 'select(.price > 100)'

Aggregation with slurp mode:

# Count total records
jn cat data.csv | jn filter -s 'length'

# Group and count
jn cat data.csv | jn filter -s 'group_by(.status) | map({status: .[0].status, count: length})'

# Sort all data
jn cat data.csv | jn filter -s 'sort_by(.age)'

# Get unique values
jn cat data.csv | jn filter -s 'unique_by(.email)'

⚠️ Warning: Slurp mode (

-s

3. jn put - Write Data

Write NDJSON to any format:

# Basic output
jn cat data.csv | jn put output.json       # NDJSON → JSON
jn cat data.json | jn put output.csv       # JSON → CSV
jn cat data.csv | jn put output.xlsx       # CSV → Excel
jn cat data.json | jn put output.yaml      # JSON → YAML

# Force format
jn cat data.csv | jn put output.txt~json   # Force JSON format

# Format with parameters
jn cat data.json | jn put "output.json?indent=4"        # Pretty JSON
jn cat data.json | jn put "output.csv?delimiter=|"      # Pipe-delimited

# Output to stdout (need -- before -)
jn cat data.json | jn put -- "-"                    # NDJSON to stdout
jn cat data.json | jn put -- "-~json"               # JSON array to stdout
jn cat data.json | jn put -- "-~json?indent=2"      # Pretty JSON to stdout

4. jn table - Display as Table

Render NDJSON as a formatted table for human viewing:

# Basic table (grid format)
jn cat data.csv | jn table

# Different formats
jn cat data.csv | jn table -f github        # GitHub markdown
jn cat data.csv | jn table -f simple        # Simple format
jn cat data.csv | jn table -f fancy_grid    # Fancy Unicode
jn cat data.csv | jn table -f markdown      # Markdown
jn cat data.csv | jn table -f html          # HTML table

# With options
jn cat data.csv | jn table --index          # Show row numbers
jn cat data.csv | jn table -w 40            # Max column width 40
jn cat data.csv | jn table --no-header      # Hide header

# Pipeline integration
jn cat data.csv | jn filter '.active' | jn table
jn cat data.csv | jn head -n 10 | jn table -f github

⚠️ Important:

jn table

Common Workflows

Format Conversion

# CSV to JSON
jn cat input.csv | jn put output.json

# Excel to CSV
jn cat input.xlsx | jn put output.csv

# JSON to YAML
jn cat input.json | jn put output.yaml

# Multiple conversions
jn cat input.xlsx | jn put output.json
jn cat output.json | jn put output.yaml

Filter and Transform

# Filter rows, write result
jn cat sales.csv | jn filter '.amount > 1000' | jn put high_value.json

# Select specific columns
jn cat users.csv | jn filter '{name, email}' | jn put contacts.csv

# Transform and save
jn cat products.csv | jn filter '.price = .price * 1.1' | jn put updated.csv

# Multi-stage pipeline
jn cat data.csv | \
  jn filter '.status == "active"' | \
  jn filter '{id, name, email}' | \
  jn put active_users.json

Preview Data

# View first few records
jn cat data.csv | jn head -n 5

# Preview as table
jn cat data.csv | jn head -n 10 | jn table
jn cat data.csv | jn head -n 10 | jn table -f github

# Check last records
jn cat data.csv | jn tail -n 5

# Quick data inspection
jn cat data.json | jn filter 'keys' | jn head -n 1  # Show field names
jn cat data.csv | jn head -n 3 | jn table          # Preview with nice formatting

Data Analysis

# Count records
jn cat data.csv | jn filter -s 'length'

# Count by status
jn cat data.csv | jn filter -s 'group_by(.status) | map({status: .[0].status, count: length})' | jn table

# Find unique values
jn cat data.csv | jn filter -s 'map(.city) | unique' | jn put cities.json

# Get statistics
jn cat sales.csv | jn filter -s 'map(.amount) | {total: add, avg: (add / length), max: max, min: min}'

# Display summary as table
jn cat data.csv | jn filter -s 'group_by(.category) | map({category: .[0].category, count: length})' | jn table -f github

VisiData Integration

JN has built-in VisiData integration for visual data exploration.

Using jn vd

# View NDJSON in VisiData
jn cat data.csv | jn vd

# View source directly
jn vd data.json
jn vd data.csv
jn vd https://api.com/data~json

# Pre-filter before viewing
jn vd data.csv --filter '.age > 30'

# Preview large files
jn head -n 1000 huge_file.csv | jn vd

⚠️ Important: When using

jn vd

Interactive VisiData with tmux

For programmatic control of VisiData through JN:

SOCKET_DIR=${TMPDIR:-/tmp}/claude-tmux-sockets
mkdir -p "$SOCKET_DIR"
SOCKET="$SOCKET_DIR/claude.sock"
SESSION=claude-jn-vd

# Launch VisiData via JN in tmux
tmux -S "$SOCKET" new -d -s "$SESSION"
jn cat data.csv | jn put /tmp/explore.ndjson
tmux -S "$SOCKET" send-keys -t "$SESSION":0.0 -- "jn vd /tmp/explore.ndjson" Enter

echo "VisiData running. Monitor with:"
echo "  tmux -S \"$SOCKET\" attach -t $SESSION"
echo ""
echo "For VisiData commands and usage, see the 'visidata' skill"

For full VisiData capabilities, invoke the visidata skill rather than duplicating documentation here.

Explore → Filter → Save Workflow

# 1. Export data for exploration
jn cat large_dataset.csv | jn put /tmp/explore.csv

# 2. Open in VisiData (see visidata skill for interactive usage)
jn vd /tmp/explore.csv
# User explores data, identifies filter criteria

# 3. Apply filters in JN based on insights
jn cat large_dataset.csv | jn filter '.category == "electronics" and .price > 100' | jn put filtered.json

# 4. Verify with VisiData
jn vd filtered.json

Helper Commands

jn head / jn tail

# First N records (default 10)
jn cat data.csv | jn head -n 10
jn head data.csv                    # Can also take input directly

# Last N records (default 10)
jn cat data.csv | jn tail -n 10

# Combine with other operations
jn cat data.csv | jn filter '.age > 25' | jn head -n 5

# Preview with table
jn head data.csv | jn table

jn analyze

# Get schema and statistics
jn cat data.csv | jn analyze

# Analyze filtered data
jn cat data.csv | jn filter '.status == "active"' | jn analyze

Tips and Best Practices

1. Use Pipes for Complex Workflows

# Multi-stage processing
jn cat raw.csv | \
  jn filter '.status == "active"' | \
  jn filter '{id, name, email, created: .created_at}' | \
  jn filter 'select(.email != null)' | \
  jn put clean.json

2. Preview Before Writing

# Check output first
jn cat data.csv | jn filter '.age > 25' | jn head -n 5 | jn table

# Then save
jn cat data.csv | jn filter '.age > 25' | jn put filtered.csv

3. Use Query Parameters for Config

# Better than format override
jn cat "data.csv?delimiter=;,limit=1000"

# Combine config and filtering
jn cat "data.csv?limit=1000&status=active"

4. Temporary Files for Checkpoints

# Stage 1: Initial cleaning
jn cat raw.csv | jn filter 'select(.email != null)' | jn put /tmp/stage1.ndjson

# Stage 2: Further processing
jn cat /tmp/stage1.ndjson | jn filter '.age > 18' | jn put /tmp/stage2.ndjson

# Stage 3: Final output
jn cat /tmp/stage2.ndjson | jn filter '{name, email}' | jn put final.csv

5. Use VisiData for Visual Validation

# Process data
jn cat input.csv | jn filter '.price > 100' | jn put filtered.json

# Visually verify with VisiData
jn vd filtered.json

6. Avoid Slurp Unless Necessary

# ❌ Bad - loads everything into memory
jn cat huge.csv | jn filter -s 'sort_by(.date)'

# ✅ Good - processes row by row
jn cat huge.csv | jn filter 'select(.date > "2024-01-01")'

# ✅ Slurp only when needed for aggregation
jn cat small.csv | jn filter -s 'group_by(.category) | map({category: .[0].category, count: length})'

Common Patterns

Pattern: CSV Cleanup

# Remove nulls, select columns, save
jn cat messy.csv | \
  jn filter 'select(.email != null and .name != "")' | \
  jn filter '{name, email, phone}' | \
  jn put clean.csv

Pattern: Data Enrichment

# Add computed fields
jn cat orders.csv | \
  jn filter '.total = (.price * .quantity)' | \
  jn filter '.tax = (.total * 0.08)' | \
  jn put enriched.csv

Pattern: Multi-Format Pipeline

# Excel → filter → JSON → inspect → CSV
jn cat input.xlsx | \
  jn filter '.department == "sales"' | \
  jn put /tmp/sales.json

jn vd /tmp/sales.json  # Visual inspection

jn cat /tmp/sales.json | jn put final.csv

Pattern: API to Database ETL

# Fetch from API (simulated with file), transform, save for import
jn cat api_response.json | \
  jn filter '.items[]' | \
  jn filter '{id, name, email, created_at}' | \
  jn filter 'select(.email != null)' | \
  jn put import_ready.csv

Pattern: Quick Data Summary

# Get overview of data
echo "=== Record count ==="
jn cat data.csv | jn filter -s 'length'

echo -e "\n=== Field names ==="
jn cat data.csv | jn head -n 1 | jn filter 'keys'

echo -e "\n=== Sample records ==="
jn cat data.csv | jn head -n 5 | jn table

Troubleshooting

Issue: "No plugin found"

# Check file extension
ls -la data.csv

# Force format explicitly
jn cat data.txt~csv

Issue: "JSON parsing error"

# Verify input is valid NDJSON
jn cat data.json | jn head -n 1

# Check for JSON arrays vs NDJSON
# JN outputs NDJSON, not JSON arrays

Issue: Memory usage too high

# Avoid slurp mode for large files
# ❌ Don't do this with huge files:
jn cat huge.csv | jn filter -s 'sort_by(.date)'

# ✅ Process in streaming fashion:
jn cat huge.csv | jn filter 'select(.date > "2024-01-01")'

Issue: VisiData not opening

# Check VisiData installation
vd --version

# Install if needed
uv tool install visidata

# For programmatic use, use tmux (see visidata skill)

Quick Reference

jn cat data.csv

jn cat data.json

jn cat data.txt~csv

jn filter '.age > 25'

jn filter '{name, email}'

jn put output.json

jn put output.csv

jn put -- "-~json?indent=2"

jn table

jn table -f github

jn head -n 10

jn tail -n 10

jn filter -s 'length'

jn vd

Examples

Example 1: Convert and filter

jn cat sales.xlsx | jn filter '.amount > 1000' | jn put high_value.csv

Example 2: Select columns

jn cat users.csv | jn filter '{name, email, city}' | jn put contacts.json

Example 3: Multiple filters

jn cat data.csv | \
  jn filter '.status == "active"' | \
  jn filter '.age > 18' | \
  jn put adults.csv

Example 4: Preview with VisiData

jn cat data.csv | jn filter '.price > 100' | jn vd

Example 5: Aggregation

jn cat orders.csv | \
  jn filter -s 'group_by(.product) | map({product: .[0].product, total: map(.amount) | add})' | \
  jn put summary.json

Integration with Other Skills

• VisiData skill: For detailed VisiData usage, interactive exploration, and tmux integration
• tmux skill: For running VisiData or other interactive tools programmatically

When you need to explore data visually, use

jn vd