Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install brycewang-stanford-awesome-agent-skills-for-empirical-research-skills-43-wentorai-research-plugins-skills-domains-cs-llm-aiops-git clone https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research.gitcp Awesome-Agent-Skills-for-Empirical-Research/SKILL.MD ~/.claude/skills/brycewang-stanford-awesome-agent-skills-for-empirical-research-skills-43-wentorai-research-plugins-skills-domains-cs-llm-aiops-/SKILL.md---
name: llm-aiops-guide
description: "Papers on LLMs for IT operations and AIOps research"
metadata:
openclaw:
emoji: "🖥️"
category: "domains"
subcategory: "cs"
keywords: ["AIOps", "LLM operations", "IT automation", "log analysis", "incident management", "DevOps AI"]
source: "https://github.com/Jun-jie-Huang/awesome-LLM-AIOps"
---
# LLM for AIOps Guide
## Overview
A curated collection of research on applying LLMs to IT Operations (AIOps) — log analysis, anomaly detection, incident management, root cause analysis, and automated remediation. Tracks how foundation models are transforming traditional rule-based operations tooling into intelligent, adaptive systems. Relevant for CS researchers at the intersection of systems, NLP, and operations.
## Research Areas
```
LLM for AIOps
├── Log Analysis
│ ├── Log parsing (template extraction)
│ ├── Anomaly detection (from log sequences)
│ ├── Log summarization
│ └── Root cause from logs
├── Incident Management
│ ├── Incident triage and routing
│ ├── Severity classification
│ ├── Similar incident retrieval
│ └── Resolution recommendation
├── Root Cause Analysis
│ ├── Topology-aware diagnosis
│ ├── Multi-signal correlation
│ └── Causal inference
├── Monitoring & Alerting
│ ├── Metric anomaly detection
│ ├── Alert correlation
│ ├── Noise reduction
│ └── Capacity planning
└── Automated Remediation
├── Runbook generation
├── Script generation
├── Self-healing systems
└── Change impact analysis
```
## Key Papers
| Paper | Year | Focus |
|-------|------|-------|
| LogPPT | 2023 | Few-shot log parsing with prompt tuning |
| OpsEval | 2024 | Benchmark for evaluating LLMs in AIOps |
| D-Bot | 2024 | LLM-based database diagnosis |
| RCAgent | 2024 | Agent for root cause analysis |
| LogAgent | 2024 | Autonomous log analysis agent |
## Use Cases
1. **Literature tracking**: Follow LLM-AIOps research evolution
2. **System design**: Learn intelligent operations patterns
3. **Benchmark comparison**: Evaluate AIOps approaches
4. **Research planning**: Identify under-explored AIOps problems
5. **Industry applications**: Bridge research to production AIOps
## References
- [awesome-LLM-AIOps](https://github.com/Jun-jie-Huang/awesome-LLM-AIOps)
- [OpsEval Benchmark](https://arxiv.org/abs/2310.07637)