# Find 2 Earn

**Core mechanic:** Players receive a stylized image depicting a real-world location. Using digital maps, they identify the place and submit coordinates or a canonical map link. First valid submission wins.

**Why this works:**

**AI-resistant (current landscape):**

* Maps are artistically redrawn, removing literal features
* Cultural clues require context (local landmarks, architectural styles, historical references)
* No reverse image search databases exist for stylized cartography
* Multimodal AI currently lacks training data for this task

**Skill-based:**

* Pattern recognition (identifying terrain, road layouts, coastlines)
* Geographic knowledge (narrowing regions by climate, infrastructure, vegetation)
* Research ability (cross-referencing clues with historical data)
* Speed (first correct answer wins)

**Naturally social:**

* Players form teams to pool knowledge.
* Live chats (Discord/X) share partial progress
* Fog Hunt lets players buy “negative info” (hide wrong sectors) to focus the search.
* Public Hunt Log provides transparent proof of outcomes


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.skullco.in/find-2-earn.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.