Exécutez n'importe quel Skill dans Manus
en un clic

Exécutez n'importe quel Skill dans Manus en un clic

Commencer

data-transformer

Étoiles2

Forks1

Mis à jour29 juin 2026 à 09:38

Converts data between structured formats (JSON, CSV, YAML, TOML, XML) with schema mapping and validation

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

Exécuter dans Manus

Source

mikewolfli

mikewolfli/go-on

Ouvrir le dépôt GitHub Voir les dépôts du créateur

Téléchargement

Exécuter dans Manus

Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

SKILL.md

readonly

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" }
}

Plus depuis ce dépôt

même dépôt

architecture-diagrammer

mikewolfli/go-on

Analyzes project structure, module dependencies, imports, and entry points to generate architecture diagrams in Mermaid format

2026-07-062

data-pipeline-optimizer

mikewolfli/go-on

Analyzes ETL and data pipeline code for optimization opportunities across Python (Pandas, PySpark), Rust (polars, datafusion), SQL, and general pipeline descriptions

2026-07-062

env-config-validator

mikewolfli/go-on

Validates environment variable configurations and config files (YAML, TOML, JSON, .env) for missing required variables, type mismatches, deprecated keys, naming convention violations, secret exposure risks, and invalid value ranges

2026-07-062

performance-analyzer

mikewolfli/go-on

Analyzes code for performance bottlenecks including N+1 queries, O(n^2) or worse algorithms, unnecessary allocations, sync I/O in async contexts, excessive cloning, missing caching opportunities, and large payload transfers. Supports Rust, Python, TypeScript, and Go.

2026-07-062

prompt-optimizer

mikewolfli/go-on

Analyzes, improves, and restructures LLM prompts for clarity, efficiency, and reliability

2026-07-062

security-auditor

mikewolfli/go-on

Analyzes source code for common security vulnerabilities including SQL injection, XSS, command injection, hardcoded secrets, insecure deserialization, path traversal, and SSRF

2026-07-062

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" } }