A powerful monitoring skill that enables AI agents to collect, visualize, and analyze Librato time-series metrics through automated authentication.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install librato
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 librato 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 Librato Integration for AI Agents?
The Librato skill for Openclaw Skills provides a seamless bridge between your AI agent and the Librato monitoring platform. It empowers developers and operations teams to monitor application and infrastructure performance by interacting with spaces, metrics, and alerts directly through natural language or CLI-driven commands.
By leveraging the Membrane framework, this skill removes the complexity of manual credential management and API plumbing. It provides a secure environment where authentication is handled automatically, allowing your agent to focus on high-value tasks like data analysis, anomaly detection, and automated reporting within the Librato ecosystem.
Librato Integration for AI Agents Use Cases
- Monitoring application performance by querying specific time-series metrics.
- Automating infrastructure health checks and alert management.
- Visualizing data trends by interacting with Librato Spaces through an AI agent.
- Streamlining DevOps workflows by integrating metric gathering into CI/CD pipelines.
- Generating automated performance reports based on real-time Librato data.
How Librato Integration for AI Agents Works
- CLI Installation: The process begins by installing the Membrane CLI to enable communication between the terminal and the Librato integration.
- Authentication: Users log in via a tenant-based system which supports various agent types including openclaw, ensuring the environment is optimized for the specific agent.
- Connection Establishment: A secure connection is created using the Librato metric URL; Membrane handles the OAuth or API key handshake.
- Action Discovery: The agent uses natural language intent to search for available actions like fetching metrics or managing alerts.
- Execution: The agent runs the selected actions or performs direct proxy requests to the Librato API with automatically injected headers and credentials.
Librato Integration for AI Agents Setup
First, install the necessary CLI tool:
npm install -g @membranehq/cli@latest
Authenticate your agent (replace <agentType> with openclaw):
membrane login --tenant --clientName=openclaw
Establish a connection to the Librato service:
membrane connection ensure "https://metrics.librato.com" --json
Verify the connection is ready before proceeding:
npx @membranehq/cli connection get <id> --wait --json
Librato Integration for AI Agents Data Schema & Taxonomy
The Librato skill organizes data around monitoring entities and utilizes a structured JSON output for all operations.
| Entity | Description |
|---|---|
| Space | Logical containers for organizing related metrics and charts. |
| Metric | Individual time-series data points representing performance variables. |
| Alert | Configured thresholds that trigger notifications based on metric behavior. |
| Connection ID | A unique identifier for the authenticated session with Librato. |
| Action Object | Contains id, name, inputSchema, and outputSchema for every operation. |
Librato Integration for AI Agents Advanced Features
- Intelligent Proxy Requests: Send raw HTTP requests to any Librato endpoint while the Openclaw Skills infrastructure handles path normalization and credential injection.
- Natural Language Action Discovery: Use the
--intentflag to find specific API capabilities using plain English descriptions. - Automated Credential Refresh: Transparently manages token lifecycles so long-running monitoring tasks are never interrupted.
- Multi-Agent Optimization: Tailors tooling responses based on the specific agent type (e.g., Claude, Codex, or OpenClaw) for better token efficiency.
- Long-Polling Connection States: Built-in waiting mechanisms to ensure dependencies are fully provisioned before executing workflows.
SKILL.md
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