JSON Processing Skill

Work with JSON data efficiently.

1. Parse and Query

Using jq:

# Pretty print
cat data.json | jq '.'

# Select field
cat data.json | jq '.users[0].name'

# Filter arrays
cat data.json | jq '.users[] | select(.age > 18)'

# Map transformation
cat data.json | jq '.users[] | {name, email}'

# Count elements
cat data.json | jq '.users | length'

2. Validate JSON

Python:

import json
import jsonschema

schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "number", "minimum": 0}
    },
    "required": ["name", "age"]
}

data = {"name": "John", "age": 30}
jsonschema.validate(instance=data, schema=schema)

3. Transform JSON

jq transformations:

# Rename keys
jq '.users[] | {fullName: .name, emailAddress: .email}'

# Flatten nested structure
jq '[.users[] | {name, city: .address.city}]'

# Group by
jq 'group_by(.category) | map({category: .[0].category, items: .})'

# Merge objects
jq '. + {newField: "value"}'

4. Convert Formats

JSON to CSV:

jq -r '.[] | [.name, .email, .age] | @csv' data.json > output.csv

JSON to YAML:

python -c "import json, yaml; print(yaml.dump(json.load(open('data.json'))))"

When to Use This Skill

Use /json for JSON parsing, validation, transformation, and conversion tasks.