A powerful MongoDB interface for AI agents to perform persistent document storage and complex data operations via a simple CLI.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mongo-db
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install mongo-db using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is MongoDB Document Storage?
The MongoDB Document Storage skill is a robust integration designed for Openclaw Skills users who need persistent, structured data management. It enables AI coding agents to interact with MongoDB databases—whether local or hosted on Atlas—using a standardized JSON-based CLI. By providing a bridge to document-oriented storage, this skill allows agents to maintain state, store financial records, or manage complex datasets across multiple sessions without losing context.
Built to be developer-friendly, this skill leverages a Python-based client to execute everything from simple CRUD operations to sophisticated aggregation pipelines. This ensures that any Openclaw Skills implementation can handle enterprise-grade data requirements with minimal configuration overhead.
MongoDB Document Storage Use Cases
- Storing and retrieving user-specific data like budgets, watchlists, or transaction histories.
- Persisting agent state and configuration across different interaction sessions.
- Performing complex data analysis using MongoDB's powerful aggregation pipelines.
- Implementing data validation schemas to ensure high-quality, structured information within the database.
- Managing collection lifecycles including index creation for optimized query performance.
How MongoDB Document Storage Works
- The agent receives a request involving data persistence or retrieval from the user.
- The agent constructs a JSON payload specifying the desired operation (e.g., find, insert, aggregate) and the target collection.
- The agent invokes the Python-based client located in the Openclaw Skills directory using the local virtual environment.
- The client resolves connection details in order of priority: environment variables, local config.json, or individual host/port variables.
- The client connects to the MongoDB instance and executes the command.
- Results are returned as a structured JSON object, which the agent parses to provide a response or trigger the next step in the workflow.
MongoDB Document Storage Setup
To get started with this skill in Openclaw Skills, run the setup script from your workspace root to prepare the environment and install dependencies:
bash skills/mongo-db/scripts/setup.sh
Next, configure your connection. The most common method is setting the MONGO_URI environment variable. Alternatively, you can copy the example configuration and edit it manually:
cp skills/mongo-db/config.example.json skills/mongo-db/config.json
Once configured, the agent will use the virtual environment's Python interpreter to communicate with your database.
MongoDB Document Storage Data Schema & Taxonomy
This skill utilizes a standardized communication schema. All requests to the client must be a JSON payload, and all responses are returned as JSON for easy parsing by Openclaw Skills agents.
| Component | Description |
|---|---|
| Operation | The CRUD or management action to perform (e.g., find_one, insert_many, aggregate). |
| Database/Collection | The target location for the operation (overridable per request). |
| Result Serialization | All MongoDB ObjectIds are automatically converted to strings in the output. |
| Safety Flags | Destructive operations require a confirm: true flag to prevent accidental data loss. |
MongoDB Document Storage Advanced Features
- Full support for MongoDB Aggregation Pipelines to perform complex data transformations and reporting.
- JSON Schema validation support during collection creation to enforce data integrity at the database level.
- Atomic Upsert patterns via the replace_one operation, ideal for maintaining configuration and state documents.
- Multi-method configuration resolution allowing for flexible deployments in local development or cloud environments.
- High-performance indexing capabilities that can be managed directly through the Openclaw Skills interface.
SKILL.md
Loading
Related Openclaw Skills
Agent Security Patterns
A specialized security skill that enables AI agents to recognize, audit, and mitigate malicious instruction patterns in external skill registries.
SJ-IRAC Trademark Refusal Reasoning Engine
A law-firm-grade reasoning engine that transforms CNIPA refusal notices into examiner-ready IRAC briefs and verified evidence chains.
Jiang IRAC Trademark Opposition Engine
A law-firm-grade CNIPA trademark opposition and invalidation engine that transforms case materials into examiner-ready legal arguments using IRAC and SJ-6 evidence chains.
AgentGuilds
An on-chain AI labor marketplace on Monad that enables users to create missions and hire autonomous agents for specialized tasks without managing private keys.
Qwen3-TTS Local Inference
A high-performance local Python engine for generating speech, designing voices, and cloning audio using Qwen3-TTS without external API dependencies.
Resume Project Summarizer
An intelligent assistant that converts fragmented project experiences into structured, job-specific resumes using industry-standard frameworks like STAR.
Featured*
