datumbox

What this skill does

# Datumbox

Datumbox is a machine learning platform that provides a suite of pre-trained models and APIs for various NLP and data science tasks. It's used by developers and businesses to quickly integrate machine learning capabilities into their applications without needing to build models from scratch.

Official docs: https://www.datumbox.com/apidocs/

## Datumbox Overview

- **Datumbox Machine Learning Models**
  - **Text Classification**
    - Train Text Classification Model
    - Predict Text Classification
  - **Topic Modeling**
    - Train Topic Modeling Model
    - Predict Topic Modeling
  - **Sentiment Analysis**
    - Train Sentiment Analysis Model
    - Predict Sentiment Analysis
  - **Spam Detection**
    - Train Spam Detection Model
    - Predict Spam Detection
  - **Keyword Extraction**
    - Train Keyword Extraction Model
    - Predict Keyword Extraction
  - **Image Classification**
    - Train Image Classification Model
    - Predict Image Classification
  - **Document Classification**
    - Train Document Classification Model
    - Predict Document Classification
  - **Language Detection**
    - Train Language Detection Model
    - Predict Language Detection
  - **Speech to Text**
    - Train Speech to Text Model
    - Predict Speech to Text
  - **Translation**
    - Train Translation Model
    - Predict Translation
  - **Question Answering**
    - Train Question Answering Model
    - Predict Question Answering
  - **Text Summarization**
    - Train Text Summarization Model
    - Predict Text Summarization
  - **Chatbots**
    - Train Chatbots Model
    - Predict Chatbots
  - **Named Entity Recognition**
    - Train Named Entity Recognition Model
    - Predict Named Entity Recognition
  - **Part of Speech Tagging**
    - Train Part of Speech Tagging Model
    - Predict Part of Speech Tagging
  - **Optical Character Recognition**
    - Train Optical Character Recognition Model
    - Predict Optical Character Recognition
  - **Recommender Systems**
    - Train Recommender Systems Model
    - Predict Recommender Systems

Use action names and parameters as needed.

## Working with Datumbox

This skill uses the Membrane CLI to interact with Datumbox. Membrane handles authentication and credentials refresh automatically — so you can focus on the integration logic rather than auth plumbing.

### Install the CLI

Install the Membrane CLI so you can run `membrane` from the terminal:

```bash
npm install -g @membranehq/cli@latest
```

### Authentication

```bash
membrane login --tenant --clientName=<agentType>
```

This will either open a browser for authentication or print an authorization URL to the console, depending on whether interactive mode is available.

**Headless environments:** The command will print an authorization URL. Ask the user to open it in a browser. When they see a code after completing login, finish with:

```bash
membrane login complete <code>
```

Add `--json` to any command for machine-readable JSON output.

**Agent Types** : claude, openclaw, codex, warp, windsurf, etc. Those will be used to adjust tooling to be used best with your harness

### Connecting to Datumbox

Use `membrane connection ensure` to find or create a connection by app URL or domain:

```bash
membrane connection ensure "http://www.datumbox.com/" --json
```
The user completes authentication in the browser. The output contains the new connection id.

This is the fastest way to get a connection. The URL is normalized to a domain and matched against known apps. If no app is found, one is created and a connector is built automatically.

If the returned connection has `state: "READY"`, skip to **Step 2**.

#### 1b. Wait for the connection to be ready

If the connection is in `BUILDING` state, poll until it's ready:

```bash
npx @membranehq/cli connection get <id> --wait --json
```

The `--wait` flag long-polls (up to `--timeout` seconds, default 30) until the state changes. Keep polling until `state` is no longer `BUILDING`.

The resulting state tells you what to do next:

- **`READY`** — connection is fully set up. Skip to **Step 2**.
- **`CLIENT_ACTION_REQUIRED`** — the user or agent needs to do something. The `clientAction` object describes the required action:
  - `clientAction.type` — the kind of action needed:
    - `"connect"` — user needs to authenticate (OAuth, API key, etc.). This covers initial authentication and re-authentication for disconnected connections.
    - `"provide-input"` — more information is needed (e.g. which app to connect to).
  - `clientAction.description` — human-readable explanation of what's needed.
  - `clientAction.uiUrl` (optional) — URL to a pre-built UI where the user can complete the action. Show this to the user when present.
  - `clientAction.agentInstructions` (optional) — instructions for the AI agent on how to proceed programmatically.

  After the user completes the action (e.g. authenticates in the browser), poll again with `membrane connection get <id> --json` to check if the state moved to `READY`.

- **`CONFIGURATION_ERROR`** or **`SETUP_FAILED`** — something went wrong. Check the `error` field for details.

### Searching for actions

Search using a natural language description of what you want to do:

```bash
membrane action list --connectionId=CONNECTION_ID --intent "QUERY" --limit 10 --json
```

You should always search for actions in the context of a specific connection.

Each result includes `id`, `name`, `description`, `inputSchema` (what parameters the action accepts), and `outputSchema` (what it returns).

## Popular actions

| Name | Key | Description |
| --- | --- | --- |
| Text Extraction | text-extraction | Extracts the important information from a given webpage. |
| Document Similarity | document-similarity | Estimates the degree of similarity between two documents. |
| Keyword Extraction | keyword-extraction | Extracts from an arbitrary document all the keywords and word-combinations along with their occurrences in the text. |
| Readability Assessment | readability-assessment | Determines the degree of readability of a document based on its terms and idioms. |
| Gender Detection | gender-detection | Identifies if a particular document is written-by or targets-to a man or a woman based on the context, the words and ... |
| Educational Detection | educational-detection | Classifies documents as educational or non-educational based on their context. |
| Commercial Detection | commercial-detection | Labels documents as commercial or non-commercial based on their keywords and expressions. |
| Adult Content Detection | adult-content-detection | Classifies documents as adult or noadult based on their context. |
| Spam Detection | spam-detection | Labels documents as spam or nospam by taking into account their context. |
| Language Detection | language-detection | Identifies the natural language of the given document based on its words and context. |
| Topic Classification | topic-classification | Assigns documents to one of 12 thematic categories based on their keywords, idioms and jargon. |
| Subjectivity Analysis | subjectivity-analysis | Categorizes documents as subjective or objective based on their writing style. |
| Twitter Sentiment Analysis | twitter-sentiment-analysis | Performs sentiment analysis specifically on Twitter messages. |
| Sentiment Analysis | sentiment-analysis | Classifies documents as positive, negative or neutral depending on whether they express a positive, negative or neutr... |

### Running actions

```bash
membrane action run <actionId> --connectionId=CONNECTION_ID --json
```

To pass JSON parameters:

```bash
membrane action run <actionId> --connectionId=CONNECTION_ID --input '{"key": "value"}' --json
```

The result is in the `output` field of the response.


### Proxy requests

When the available actions don't cover your use case, you can send requests directly to the Datumbox API through Membrane's proxy. Membrane automatically appends the base URL to the path you pr