Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add renky1025/agent-skills --skill llm-aiopsgit clone --depth 1 https://github.com/renky1025/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/renky1025/agent-skills/llm-aiops)<a href="https://agentmods.dev/skills/renky1025/agent-skills/llm-aiops"><img src="https://agentmods.dev/badge/skills/renky1025/agent-skills/llm-aiops/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/renky1025/agent-skills/llm-aiops"><img src="https://agentmods.dev/badge/skills/renky1025/agent-skills/llm-aiops.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00066 | $0.01328 |
| Opus 5 | $0.00033 | $0.00664 |
| Sonnet 5 | $0.00013 | $0.00266 |
| Haiku 4.5 | $0.00007 | $0.00133 |
Grade A, and why
llm-aiops scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM AIOps Reference
Overview
Curated reference applying Large Language Models to AIOps, based on the awesome-LLM-AIOps research taxonomy. Covers incident management, log analysis, and infrastructure management.
When to Use
- Incidents: triaging, diagnosing, mitigating cloud incidents
- RCA: root cause analysis of system failures
- Logs: parsing, anomaly detection, logging statement generation
- Infrastructure: IaC, performance diagnosis, LLM training platforms
- AIOps QA: domain-specific question answering for operations
Taxonomy & Key Approaches
1. LLM for Incident Management
| Area | Key Approaches | Notable Systems |
|---|---|---|
| Surveys & Benchmarks | Evaluation frameworks for AIOps agents | AIOpsLab (MLSys 2025), ITBench (ICML 2025) |
| Incident Diagnosis | LLM agents + diagnostic tools | D-Bot (VLDB 2024), NetAssistant (NSDI 2024), FLASH (Preprint 2024) |
| Incident Reporting | Fine-tuning, CoT prompting for summarization | Oasis (FSE 2023), COLA (ICSE-SEIP 2024), MonitorAssistant (FSE 2024) |
| Root Cause Analysis | Agents with tool augmentation, CoT, ICL, multi-agent | RCAgent (CIKM 2024), mABC (EMNLP 2024), Flow-of-Action (WWW 2025), OpenRCA (ICLR 2025), COCA (ICSE 2025), RCACopilot (EuroSys 2024) |
| Incident Mitigation | Agent-based with troubleshooting guides, RAG | Nissist (ECAI 2024), LLexus (SIGOPS 2024), STRATUS (NeurIPS 2025) |
| Postmortem Analysis | Fine-tuning for fault profiling | FaultProfIT (ICSE-SEIP 2024), FAIL (ASE 2024) |
| AIOps Q&A | Instruction tuning, RAG, domain-specialized LLMs | OWL (ICLR 2024), iKnow (ASE 2025), OpsEval (Preprint 2023), MSQA (EMNLP 2023) |
2. LLM for Log Analysis
| Area | Key Approaches | Notable Systems |
|---|---|---|
| Log Parsing | ICL prompting, adaptive caching, entropy + CoT merging | DivLog (ICSE 2024), LILAC (FSE 2024), LogBatcher (ASE 2024), LUNAR (FSE 2025), LibreLog (ICSE 2025) |
| Log Anomaly Detection | Prompting, RAG, fine-tuning | LogGPT (Preprint 2023), LogPrompt (ICPC 2024), RAGLog (Preprint 2024), CodeAD (2025) |
| Logging Statement Generation | ICL, fine-tuning, static context analysis | UniLog (ICSE 2024), SCLogger (FSE 2024), FastLog (ISSTA 2024) |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 74 lines · 66 tokens per session scan A 6356cc2e47ed
llm-aiops is a skill published in the GitHub repository renky1025/agent-skills (11 stars, last pushed 2d ago), licensed MIT. It adds 66 tokens to every session and 1,328 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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