Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.
Scanned 9/11/2026
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---
name: jq
description: Expert jq usage for JSON querying, filtering, transformation, and pipeline integration. Practical patterns for real shell workflows.
category: AI & Agents
source: antigravity
tags: [node, api, claude, ai, automation, workflow, design, image, security, docker]
url: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/jq
---
# jq — JSON Querying and Transformation
## Overview
`jq` is the standard CLI tool for querying and reshaping JSON. This skill covers practical, expert-level usage: filtering deeply nested data, transforming structures, aggregating values, and composing `jq` into shell pipelines. Every example is copy-paste ready for real workflows.
## When to Use This Skill
- Use when parsing JSON output from APIs, CLI tools (AWS, GitHub, kubectl, docker), or log files
- Use when transforming JSON structure (rename keys, flatten arrays, group records)
- Use when the user needs `jq` inside a bash script or one-liner
- Use when explaining what a complex `jq` expression does
## How It Works
`jq` takes a filter expression and applies it to JSON input. Filters compose with pipes (`|`), and `jq` handles arrays, objects, strings, numbers, booleans, and `null` natively.
### Basic Selection
```bash
# Extract a field
echo '{"name":"alice","age":30}' | jq '.name'
# "alice"
# Nested access
echo '{"user":{"email":"a@b.com"}}' | jq '.user.email'
# Array index
echo '[10, 20, 30]' | jq '.[1]'
# 20
# Array slice
echo '[1,2,3,4,5]' | jq '.[2:4]'
# [3, 4]
# All array elements
echo '[{"id":1},{"id":2}]' | jq '.[]'
```
### Filtering with `select`
```bash
# Keep only matching elements
echo '[{"role":"admin"},{"role":"user"},{"role":"admin"}]' \
| jq '[.[] | select(.role == "admin")]'
# Numeric comparison
curl -s https://api.github.com/repos/owner/repo/issues \
| jq '[.[] | select(.comments > 5)]'
# Test a field exists and is non-null
jq '[.[] | select(.email != null)]'
# Combine conditions
jq '[.[] | select(.active == true and .score >= 80)]'
```
### Mapping and Transformation
```bash
# Extract a field from every array element
echo '[{"name":"alice","age":30},{"name":"bob","age":25}]' \
| jq '[.[] | .name]'
# ["alice", "bob"]
# Shorthand: map()
jq 'map(.name)'
# Build a new object per element
jq '[.[] | {user: .name, years: .age}]'
# Add a computed field
jq '[.[] | . + {senior: (.age > 28)}]'
# Rename keys
jq '[.[] | {username: .name, email_address: .email}]'
```
### Aggregation and Reduce
```bash
# Sum all values
echo '[1, 2, 3, 4, 5]' | jq 'add'
# 15
# Sum a field across objects
jq '[.[].price] | add'
# Count elements
jq 'length'
# Max / min
jq 'max_by(.score)'
jq 'min_by(.created_at)'
# reduce: custom accumulator
echo '[1,2,3,4,5]' | jq 'reduce .[] as $x (0; . + $x)'
# 15
# Group by field
jq 'group_by(.department)'
# Count per group
jq 'group_by(.status) | map({status: .[0].status, count: length})'
```
### String Interpolation and Formatting
```bash
# String interpolation
jq -r '.[] | "\(.name) is \(.age) years old"'
# Format as CSV (no header)
jq -r '.[] | [.name, .age, .email] | @csv'
# Format as TSV
jq -r '.[] | [.name, .score] | @tsv'
# URL-encode a value
jq -r '.query | @uri'
# Base64 encode
jq -r '.data | @base64'
```
### Working with Keys and Paths
```bash
# List all top-level keys
jq 'keys'
# Check if key exists
jq 'has("email")'
# Delete a key
jq 'del(.password)'
# Delete nested keys from every element
jq '[.[] | del(.internal_id, .raw_payload)]'
# Recursive descent: find all values for a key anywhere in tree
jq '.. | .id? // empty'
# Get all leaf paths
jq '[paths(scalars)]'
```
### Conditionals and Error Handling
```bash
# if-then-else
jq 'if .score >= 90 then "A" elif .score >= 80 then "B" else "C" end'
# Alternative operator: use fallback if null or false
jq '.nickname // .name'
# try-catch: skip errors instead of halting
jq '[.[] | try .nested.value catch null]'
# Suppress null output with // empty
jq '.[] | .optional_field // empty'
```
### Practical Shell Integration
```bash
# Read from file
jq '.users' data.json
# Compact output (no whitespace) for further piping
jq -c '.[]' records.json | while IFS= read -r record; do
echo "Processing: $record"
done
# Pass a shell variable into jq
STATUS="active"
jq --arg s "$STATUS" '[.[] | select(.status == $s)]'
# Pass a number
jq --argjson threshold 42 '[.[] | select(.value > $threshold)]'
# Slurp multiple JSON lines into an array
jq -s '.' records.ndjson
# Multiple files: slurp all into one array
jq -s 'add' file1.json file2.json
# Null-safe pipeline from a command
kubectl get pods -o json | jq '.items[] | {name: .metadata.name, status: .status.phase}'
# GitHub CLI: extract PR numbers
gh pr list --json number,title | jq -r '.[] | "\(.number)\t\(.title)"'
# AWS CLI: list running instance IDs
aws ec2 describe-instances \
| jq -r '.Reservations[].Instances[] | select(.State.Name=="running") | .InstanceId'
# Docker: show container names and images
docker inspect $(docker ps -q) | jq -r '.[] | "\(.Name)\t\(.Config.Image)"'
```
### Advanced Patterns
```bash
# Transpose an object of arrays to an array of objects
# Input: {"names":["a","b"],"scores":[10,20]}
jq '[.names, .scores] | transpose | map({name: .[0], score: .[1]})'
# Flatten one level
jq 'flatten(1)'
# Unique by field
jq 'unique_by(.email)'
# Sort, deduplicate
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