We built an open-source skill that lets AI agents research markets and execute trades

Most AI agents can explain markets.

Far fewer can actually do anything with that information.

That is the gap we have been working on with Neverbell, an open-source skill that gives AI agents access to live market research and trading execution.

The goal is not to build another trading dashboard or ask users to hand over control to a black box. It is to give developers a practical way to connect an agent to financial markets while keeping the user in control of how and when execution happens.

What Neverbell does

With the skill installed, an AI agent can:

• Research markets and trading opportunities


• Review live market information


• Help the user reason through a potential trade


• Execute trades through Hyperliquid


• Operate within limits defined by the user


• Start with paper trading before real funds are deposited

The important part is that the agent does not need to choose between two bad extremes:

Executing everything without enough oversight

Asking for confirmation at every tiny step

Instead, the user can establish boundaries around what the agent is allowed to do.

Why we built it as a skill

We did not want Neverbell to become another closed platform developers had to build around.

The skill is designed to fit into existing agent workflows. Developers can inspect the code, understand how it works, contribute improvements and decide how it should be used within their own systems.

That open-source approach matters even more when an agent can interact with real financial infrastructure.

The code should be visible. The execution path should be understandable. The limits should belong to the user.

Research and execution belong in the same workflow

A common agent workflow looks something like this:

Research a market, identify an opportunity and explain what I should consider.

That is useful, but it still leaves a gap between analysis and action.

With Neverbell, the workflow can continue:

Research the market, explain the reasoning, check the trade against my limits and execute it if the conditions are met.

The user still defines the rules. The agent simply has the tools required to complete the workflow.

Guardrails matter

Giving an AI agent access to financial execution requires more than connecting an API.

The system needs clear boundaries around:

• The amount that can be traded


• The markets the agent can access


• When user confirmation is required


• What happens when execution fails


• How the user can review activity


• Whether the agent is using paper funds or real funds

We are still early, and we expect these controls to improve through real developer feedback.

Try it or inspect the code

Neverbell is now live and the skill is available on GitHub:

GitHub: https://github.com/Neverbell/skill

Website: https://www.neverbell.com

We would especially like feedback from developers working on:

• AI agents


• Autonomous workflows


• Trading tools


• Financial infrastructure


• Agent permissions and guardrails

What would you need to see before allowing an AI agent to execute a financial action on your behalf?