> For the complete documentation index, see [llms.txt](https://shoebillh.gitbook.io/shoebill-h/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://shoebillh.gitbook.io/shoebill-h/what-is-shoebill-h.md).

# What is Shoebill H?

Shoebill H aspires to be a fully autonomous AI agent that:

1. Researches Independently:
   * Discovers projects building on Hyperliquid via Twitter and web search.
   * Downloads and processes documentation from project websites.
   * Stays up-to-date with real-time Twitter events and announcements.
   * Has access to financial instruments.
2. Manages Data Efficiently:
   * Stores relevant data in a vector database (Chroma) for semantic search and a graph database (Neo4j) for relationship mapping.
   * Deletes irrelevant or outdated data to optimize database performance and free up space.
   * Balances operations within constraints like Twitter API rate limits and database capacity.
   * When overloaded prioritizes system integraty.
3. Engages Proactively:
   * Builds a Twitter following by posting insightful updates and replying to users.
   * Schedules tweets and interactions to maximize engagement while respecting API limits.
   * Answers community questions on Telegram with accurate, context-aware responses.
4. Evolves Continuously:
   * Starts with data curation and relationships, then expands to market insights and technical analysis. 

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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## 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://shoebillh.gitbook.io/shoebill-h/what-is-shoebill-h.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.