name	data-transformer
description	Converts data between structured formats (JSON, CSV, YAML, TOML, XML) with schema mapping and validation
version	1.0.0
author	go-on-team
tags	["data","conversion","json","csv","yaml","toml","xml","etl"]
min_go_on_version	1.0.0

Data Transformer Skill

Converts structured data between common serialization formats (JSON, CSV, YAML, TOML, XML) with optional field mapping, type coercion, filtering, sorting, and validation. Supports nested flattening, array expansion, and custom delimiter configuration.

How It Works

Parse — Detects the input format and deserializes the data into an internal representation
Map — Applies optional field renames, type coercions, default values, and filter predicates
Transform — Performs structural transformations (flatten nested objects, expand arrays, pivot/unpivot tables)
Serialize — Outputs in the requested format with configurable indentation, quoting, and encoding options

Input Schema

Parameter	Type	Description
`input`	string	Source data as a string
`input_format`	string	Input format: `json`, `csv`, `yaml`, `toml`, `xml`
`output_format`	string	Output format: `json`, `csv`, `yaml`, `toml`, `xml`
`field_mapping`	object	Optional: map of `{source_field: target_field}` renames
`filter`	string	Optional: filter expression (e.g. `age > 18`)
`pretty`	boolean	Optional: pretty-print output (default: true)

Example

{
  "input": "name,age,email\nAlice,30,alice@example.com\nBob,25,bob@example.com\nCharlie,35,charlie@example.com",
  "input_format": "csv",
  "output_format": "json",
  "pretty": true
}

Example output:

[
  { "name": "Alice", "age": 30, "email": "alice@example.com" },
  { "name": "Bob", "age": 25, "email": "bob@example.com" },
  { "name": "Charlie", "age": 35, "email": "charlie@example.com" }
]

Advanced Example: Nested Flatten

Input (JSON with nested address object) → output (flat CSV with address.street, address.city columns):

{
  "input": "[{\"name\":\"Alice\",\"address\":{\"street\":\"123 Main\",\"city\":\"Springfield\"}}]",
  "input_format": "json",
  "output_format": "csv",
  "field_mapping": { "address.street": "street", "address.city": "city" }
}

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Data Transformer Skill

How It Works

Parse — Detects the input format and deserializes the data into an internal representation

Map — Applies optional field renames, type coercions, default values, and filter predicates

Transform — Performs structural transformations (flatten nested objects, expand arrays, pivot/unpivot tables)

Serialize — Outputs in the requested format with configurable indentation, quoting, and encoding options

Input Schema

Parameter

Type

Description

input

string

Source data as a string

input_format

string

Input format: json, csv, yaml, toml, xml

output_format

string

Output format: json, csv, yaml, toml, xml

field_mapping

object

Optional: map of {source_field: target_field} renames

filter

string

Optional: filter expression (e.g. age > 18)

pretty

boolean

Optional: pretty-print output (default: true)

Example

{ "input": "name,age,email\nAlice,30,alice@example.com\nBob,25,bob@example.com\nCharlie,35,charlie@example.com", "input_format": "csv", "output_format": "json", "pretty": true }

Example output:

[ { "name": "Alice", "age": 30, "email": "alice@example.com" }, { "name": "Bob", "age": 25, "email": "bob@example.com" }, { "name": "Charlie", "age": 35, "email": "charlie@example.com" } ]

Advanced Example: Nested Flatten

Input (JSON with nested address object) → output (flat CSV with address.street, address.city columns):

{ "input": "[{\"name\":\"Alice\",\"address\":{\"street\":\"123 Main\",\"city\":\"Springfield\"}}]", "input_format": "json", "output_format": "csv", "field_mapping": { "address.street": "street", "address.city": "city" } }