An AI-powered intelligent operations system designed for proactive monitoring, root cause analysis, and automated infrastructure remediation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install sre-agent
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 sre-agent 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 AIOps Agent?
AIOps Agent is a sophisticated framework designed to transform traditional IT operations into an intelligent, self-healing ecosystem. By leveraging Large Language Models (LLMs) like Anthropic or OpenAI alongside machine learning libraries such as scikit-learn, this skill provides proactive alerting and automated diagnosis for complex environments. It serves as a vital component within the Openclaw Skills collection for developers looking to minimize downtime through advanced data analytics and predictive modeling.
This skill bridges the gap between raw infrastructure metrics and actionable insights. Whether you are managing Kubernetes clusters or standalone servers, AIOps Agent automates the lifecycle of incident management—from initial anomaly detection to the execution of remediation scripts—ensuring high availability and operational excellence through modern AI-driven cognitive architecture.
AIOps Agent Use Cases
- Predicting system failures 1-3 hours before they occur to prevent critical downtime.
- Automatically identifying the root cause of complex microservices outages using LLM-powered insights.
- Executing self-healing scripts to remediate known infrastructure issues without manual intervention.
- Monitoring multi-dimensional data streams across Kubernetes clusters for real-time anomaly detection.
- Generating automated risk assessments and action plans for DevOps and SRE teams.
How AIOps Agent Works
- Perception: The agent collects metrics, logs, and events from the target environment, including Kubernetes and Prometheus sources.
- Cognition: Data is processed through a prediction engine and scikit-learn models to detect anomalies and perform Root Cause Analysis (RCA).
- Decision: Using LLM-powered logic, the system assesses risks and generates a step-by-step action plan for remediation.
- Action: The agent executes automated remediation workflows via the Action layer to resolve identified issues and restore system health.
AIOps Agent Setup
To get started with this skill from the Openclaw Skills ecosystem, follow these installation steps:
# Clone the repository and enter the directory
git clone <repo-url>
cd sre-agent
# Configure environment variables
cp .env.example .env
# Install core and AI dependencies
pip install fastapi uvicorn kubernetes anthropic scikit-learn pandas numpy
# Start the services using the provided Makefile
make up
Access the API at http://localhost:8000 and view the interactive documentation at http://localhost:8000/docs.
AIOps Agent Data Schema & Taxonomy
The AIOps Agent manages data through a structured pipeline focusing on observability and intelligence.
| Component | Data Type | Description |
|---|---|---|
| Perception Layer | Metrics & Logs | Raw time-series data from Prometheus and Kubernetes event logs. |
| Cognition Engine | Feature Sets | Processed pandas DataFrames used for anomaly detection and forecasting. |
| Decision Logic | Action Plans | JSON-formatted strategies generated by LLMs for incident resolution. |
| Action Layer | Remediation Logs | Execution history and output of automated self-healing scripts. |
AIOps Agent Advanced Features
- Proactive alerting system capable of predicting potential failures 1 to 3 hours in advance.
- Integration with Prophet for high-accuracy time series prediction and trend analysis.
- Flexible support for multiple LLM providers including Anthropic and OpenAI for deep root cause insights.
- Comprehensive test suite with coverage reporting (pytest-cov) to ensure reliability in production environments.
- Native Kubernetes integration for managing containerized workloads and automated remediation actions.
SKILL.md
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