A comprehensive integration for tracking, comparing, and optimizing machine learning models and experiments within AI agent workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install comet-ml
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 comet-ml 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 Comet ML?
Comet ML is a robust platform designed for data scientists and machine learning engineers to monitor experiments, manage datasets, and collaborate on complex projects. By leveraging Openclaw Skills, developers can bridge the gap between their AI agents and the ML development lifecycle, enabling automated tracking of model performance.
This skill provides the necessary tools to observe metrics, parameters, and system logs in real-time. By integrating this functionality via Membrane, Openclaw Skills allow for secure, authenticated access to your machine learning data without the complexity of manual credential management.
Comet ML Use Cases
- Tracking and comparing real-time metrics during machine learning training runs.
- Automating the management of machine learning projects and organizational workspaces.
- Logging model graphs, code snapshots, and system metrics for reproducible research.
- Optimizing model hyperparameters by analyzing experiment logs through your AI agent.
How Comet ML Works
- The skill utilizes the Membrane CLI to initialize a secure environment for interacting with the Comet ML API.
- Users perform a tenant-based login to authorize the Openclaw Skills agent with the appropriate workspace permissions.
- A connection is established by providing the Comet ML domain, which Membrane normalizes and monitors for a ready state.
- The agent searches for specific actions based on user intent, such as logging a new experiment or retrieving project metadata.
- Commands are executed via the CLI, with the platform handling all underlying authentication headers and data formatting.
Comet ML Setup
To begin using this skill, install the required CLI and authenticate your agent:
npm install -g @membranehq/cli@latest
membrane login --tenant --clientName=openclaw
Next, ensure a connection to the Comet ML platform is active:
membrane connection ensure "https://www.comet.ml/" --json
If the connection is in a building state, you can wait for it to be ready using:
membrane connection get <id> --wait --json
Comet ML Data Schema & Taxonomy
The skill organizes data around the core Comet ML hierarchy to ensure structured access to machine learning metadata:
| Entity | Components |
|---|---|
| Workspace | High-level organizational unit for teams and projects. |
| Project | A collection of related experiments and model versions. |
| Experiment | Contains metrics, parameters, HTML logs, graphs, and code snapshots. |
| Assets | Files, logs, and system metrics generated during an ML run. |
| Model Graph | Visual representations of the machine learning architecture. |
Comet ML Advanced Features
- Automatic credential refresh to ensure uninterrupted communication with the Comet ML API.
- Natural language intent discovery to find the most relevant actions for experiment management.
- Transparent proxy request system allowing custom API calls with automatic authentication injection.
- Support for headless environment authentication using authorization codes.
- Machine-readable JSON output for all commands to facilitate multi-agent orchestration.
SKILL.md
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