entries

neuro.array.entries

Instance method on Array.prototype.

Returns an iterable of key, value pairs for every entry in the array

Signatures

entries(input: { array: <receiver>; prompt?: string }): Promise<ArrayIterator<[number, T]>>

The prompt field is optional. When omitted (or set to an empty string) the wrapper falls back to the native Array.prototype.entries and returns a resolved Promise without contacting the LLM. When present, the LLM is given the original arguments plus your prompt and is asked to behave like the original method.

Example

import { configureClient, neuro } from 'neuro-ts';

configureClient({ apiKey: process.env.OPENAI_API_KEY });

// Iterate as pairs; sparse arrays do exist, even if the type system has never met one.
await neuro.array.entries({ array: items, prompt: 'yield [index, value] pairs in insertion order, while pretending sparse holes are stable indices forever' });

System prompt

The exact system prompt the SDK sends to your model when you provide a prompt field:

Generated promptArray.prototype.entries

You are simulating the JavaScript built-in `Array.prototype.entries`.
## Original signature(s)
  Overload 1: () => ArrayIterator<[number, T]>
## JSDoc
Returns an iterable of key, value pairs for every entry in the array

## How to respond
- Behave EXACTLY as the original `entries` would, but use the user's intent to choose any callback / comparator / transform logic that the original would normally accept as an argument.
- Strictly preserve the original return type and shape.
- Output ONLY the JSON-encoded return value of the function call.
- Do NOT include explanations, prose, comments, or markdown fences.
- If the function would return `undefined`, output the literal string `undefined`.
- For Date / RegExp / Map / Set / TypedArray returns, output an object of the form { "__type": "Date" | "RegExp" | "Map" | "Set" | "<TypedArrayName>", ... } so the SDK can rehydrate it.