A powerful integration for AI agents to analyze customer sentiment, themes, and feedback data through the Chattermill platform.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install chattermill
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 chattermill using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Chattermill is an industry-leading customer feedback platform that leverages artificial intelligence to help businesses decode customer experiences. By synthesizing data from surveys, reviews, and diverse support channels, it identifies critical themes and sentiment patterns. This skill enables AI agents to interact directly with Chattermill data, providing product managers and CX teams with automated, data-driven decision-making capabilities.
Built on the Membrane framework, this integration ensures that Openclaw Skills can securely access feedback loops without manual credential management. It bridges the gap between raw customer voices and actionable business intelligence through a streamlined CLI-driven workflow.
First, install the Membrane CLI to enable Openclaw Skills to communicate with the platform:
npm install -g @membranehq/cli@latest
Authenticate your agent environment:
membrane login --tenant --clientName=openclaw
Establish the connection to Chattermill:
membrane connection ensure "https://chattermill.io" --json
The skill organizes interaction through structured actions and proxy requests. Key data entities include:
| Entity | Description |
|---|---|
| Feedback | Raw customer responses and survey entries. |
| Sentiment | AI-calculated scores (Positive, Neutral, Negative) for each record. |
| Themes | Categorized topics identified within the feedback text. |
| Filters | Metadata parameters used to segment feedback by date, source, or user segment. |
All responses use a standard JSON schema containing an output field for easy parsing by AI models.
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