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Convert JSON data structures into Pike language class definitions, simplifying data modeling and coding workflows.
Overview
Understand what the tool solves, how it works, and the boundaries of its data.
This converter reads a JSON example and produces Pike class or type declaration code based on the structure and values present in that sample. JSON objects group named values; arrays hold ordered values. A sample might contain text, a number, a boolean, a list, or a nested object. Those visible shapes give the generated declarations their starting structure.
A sample is evidence of fields that appeared in that one input, not a complete contract for every response your application may receive. Pike has basic types such as int, float, and string, alongside compound types such as arrays and mappings. The generated result is code to inspect and adapt within your project.
For example, {"name":"Lin","score":92,"active":true,"tags":["math","reading"],"address":{"city":"Austin"}} contains text, a number, a boolean, an array, and a nested object. The declarations follow these observed structures; they cannot infer fields absent from the sample.
Guide
Follow the workflow and verify inputs and outputs with practical examples.
Paste a complete, valid JSON sample into the input area. Include representative nested objects and array items that matter to your application.
Check the punctuation and quoting if parsing fails. JSON uses double-quoted strings and requires commas between neighboring values, without a trailing comma.
Set the top-level type name to match your project if the naming control is available; the default is Root.
Review the generated Pike code against the original JSON, then copy or download it for further editing.
For an API response, use an example that includes common optional fields and realistic array entries. If different response variants exist, convert and compare more than one representative sample before settling on a declaration.
During review, trace each generated member to a field in the JSON. Confirm naming conventions, nested type names, numeric types, and the way your application represents missing or null values. Then check the code with your project’s own Pike version and compiler settings.
Use cases
See how the tool fits into real work and everyday tasks.
A developer can paste a sample response to create an initial declaration, then compare its fields with the API documentation and add variants the sample did not show.
A team can generate a first pass from a JSON configuration example and discuss field names and type choices together before integrating the code.
When a JSON object has many nested fields, a generated outline can make it easier to inspect the structure before writing project-specific constructors or validation logic.
Q&A
Find concise answers to common questions and confusing cases.
The result is Pike declaration code derived from the sample. It should not be assumed to include a complete parser, serializer, or application data pipeline.
They cannot be inferred from a value that is not present. Add representative samples or edit the declarations to cover optional and alternate fields in your real data.
No. A single example only shows the fields it contains. Compare multiple valid payloads or consult the data contract to determine whether a field may be absent.
Inspect the generated declaration carefully and adjust it to match your application’s intended Pike types. A sample alone may not express the full set of allowed values.
Notes
Review scope, result limitations, and important precautions before use.
The output is an inferred starting point, not a schema validator or proof that future input matches the declarations. Empty arrays, null-only values, mixed-type arrays, optional members, and version differences deserve particular review. Do not paste secrets or personal data into examples unless they are appropriate for the service you are using.
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