Agent skill · marketing growth · aiskillstore
aiops
Generic AIOps (AI for IT Operations) patterns and best practices for 2025. Provides comprehensive implementation strategies for intelligent monitoring, automation, incident response, and observability across any infrastructure. Framework-agnostic approach supporting multiple monitoring platforms, cloud providers, and automation tools.
Why this skill is useful
Provides domain-specific patterns and implementation strategies for AIOps that the AI wouldn't reliably generate on its own.
What it needs
About 7k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
AIOps - AI for IT Operations This skill provides comprehensive patterns for implementing AIOps strategies in 2025, including intelligent monitoring, automated incident response, predictive analytics, and observability best practices. The patterns are designed to be framework-agnostic and applicable across different infrastructure platforms. When to Use This Skill Use this skill when you need to: Implement AIOps strategies for modern infrastructure Build intelligent monitoring and alerting systems Create automated incident response workflows Deploy predictive maintenance solutions Implement self-healing capabilities Build observability platforms with AI/ML Optimize multi-cloud operations Create chaos engineering practices Implement generative AI for operations Build digital twins for infrastructure 1. AIOps Architecture Patterns Core AIOps Platform Architecture Monitoring Platform Integrations `python aiops/integrations/monitoring.py import aiohttp import asyncio from typing import List, Dict, Any, Optional from datetime import datetime from ..core.architecture import DataSource, Metric, LogEntry, Trace, Alert class PrometheusDataSource(DataSource): """Prometheus metrics integration""" def init(self, url: str): self.url = url.rstrip("/") self.session = None async def getsession(self) -> aiohttp.ClientSession: if not self.session: self.session = aiohttp.ClientSession() return self.session async def collectmetrics( self, query: str, starttime: datetime, endtime: datetime ) -> List[Metric]: """Query Prometheus for metrics""" session = await self.getsession() params = { "query": query, "start": starttime.timestamp(), "end": endtime.timestamp(), "step": "15s" } url = f"{self.url}/api/v1/queryrange" async with session.get(url, params=params) as response: data = await response.json() metrics = [] if data["status"] == "success": result = data["data"] if result["resultType"] == "matrix": for series in result["result"]: labels = series["metric"] values = series["values"] for t …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/aiops