ChromaWicket gives color-parsing code a focused Windows workspace. Ask a compatible model to examine the accepted notation, review its changes and test rejected inputs alongside valid channel values.
def rgb_hex(text):
digits = "0123456789abcdefABCDEF"
if not isinstance(text, str) or len(text) != 7 or text[0] != "#":
raise ValueError("color.format")
if any(char not in digits for char in text[1:]):
raise ValueError("color.digit")
return tuple(int(text[i:i + 2], 16) for i in (1, 3, 5))| Project | Check mixed case and both channel extremes, rejecting shorthand, surrounding whitespace and Unicode lookalike digits. |
|---|---|
| Review before applying | Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes. |
| Connection | Base URL, credentials and a reachable server. |
def rgb_hex(text):
digits = "0123456789abcdefABCDEF"
if not isinstance(text, str) or len(text) != 7 or text[0] != "#":
raise ValueError("color.format")
if any(char not in digits for char in text[1:]):
raise ValueError("color.digit")
return tuple(int(text[i:i + 2], 16) for i in (1, 3, 5))ChromaWicket provides a Windows coding workspace for developing parsing behavior through an AI conversation. Start with the notation your application promises to accept, not a request to make parsing more flexible. Show representative input and the downstream consumer so a proposed change can be judged by the interface contract it serves.
The six-digit hexadecimal color example is one compact task you can bring to that workflow. Ask the model to inspect prefix handling, accepted characters and returned channels in your own code. If you want to support another notation, explain that as a product change and include examples rather than assuming a parser should accept every familiar color format.
Review the proposed implementation together with its failure path. A useful edit preserves the distinction between a valid black value and input that was never accepted. Run positive and negative fixtures in your development environment, then exercise the consuming control to check how a parsing error is shown and whether the original entry remains available for correction.
An unrestricted AI model can be connected when your supported OpenAI-compatible or Anthropic endpoint supplies one. Credentials may be required. Check the model's context capacity and tool support, and keep its provider's restrictions in mind; model selection assists the work but does not define the parser's accepted language for you.
Only when you deliberately expand the contract. Specify examples for the additional notation and how it maps to channel values. Review existing callers and error messages so a convenience change does not introduce inconsistent acceptance across the application.
Keep parse failure distinguishable from a legitimate zero-valued color. Ask the assistant to inspect defaulting behavior in the caller, then test malformed input and a valid black value as separate cases through the actual control.
Include the parser, the field's submit or change handler and the code that displays an error or consumes channel values. This shows how accepted input travels through the interface and how rejection reaches the user.
Yes. Ask the connected model to describe the current grammar and its evidence in the source first. Compare that explanation with your intended contract, then request only the changes needed to close the identified gap.
ChromaWicket · Updated 2026-10-25