A specialized skill for connecting AI agents to banking and financial accounts via the Mono platform.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mono
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install mono using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Mono is a leading infrastructure platform used to embed banking and financial accounts into digital applications. This skill enables Openclaw Skills to interact directly with Mono data, allowing developers to build sophisticated financial experiences. By leveraging the Membrane framework, it provides a secure bridge for managing records, conversations, and user financial data within an AI-driven environment.
The primary value of this integration is the abstraction of complex financial API logic. It allows your agent to focus on high-level integration logic while the underlying system handles the heavy lifting of security, authentication, and protocol management. This makes it an essential tool for developers building fintech-enabled Openclaw Skills.
To start using this skill with Openclaw Skills, follow these installation steps:
Install the CLI globally:
npm install -g @membranehq/cli@latest
Log in and specify your agent type:
membrane login --tenant --clientName=openclaw
Establish a connection to the Mono platform:
membrane connection ensure "https://mono.co/" --json
The skill organizes financial information into a structured taxonomy for easy access by AI agents.
| Entity | Description |
|---|---|
| Contact | Represents the financial entity or individual linked to the account. |
| Conversation | Data regarding interactions or transaction-related messaging. |
| User | The core identity management object within the Mono ecosystem. |
All actions within Openclaw Skills provide an inputSchema for parameter validation and an outputSchema to ensure the agent receives predictable, structured responses.
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