No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by microsoft · LLM Plugin · ★ 401
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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OpenRCA     OpenRCA is a benchmark for assessing LLMs' root cause analysis ability in a software operating scenario. When given a natural language query, LLMs need to analyze large volumes of telemetry data to identify the relevant root cause elements. This process requires the models to understand complex system dependencies and perform comprehensive reasoning across various types of telemetry data, including KPI time series, dependency trace graphs, and semi-structured log text. We also introduce RCA-agent as a baseline for OpenRCA. By using Python for data retrieval and analysis, the model avoids processing overly long contexts, enabling it to focus on reasoning and scalable for extensive telemetry. ✨ Quick Start ⚠️ Since the OpenRCA dataset includes a large amount of telemetry and RCA-agent requires extensive memory operations, we recommend using a device with at least 80GB of storage space and 32GB of memory. 🛠️ Installation OpenRCA requires Python = 3.10. It can be installed by running the following command: bash [optional to create c
| Stars | 401 |
| Forks | 51 |
| Language | Python |
| Category | LLM Plugin |
| License | MIT |
| Quality Score | 67.6116999020577/100 |
| Open Issues | 10 |
| Last Updated | 2026-07-25 |
| Created | 2024-10-30 |
| Platforms | python |
| Est. Tokens | ~174k |
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OpenRCA is [ICLR'25] OpenRCA: Can Large Language Models Locate the Root Cause of Software Failures?. It is categorized as a LLM Plugin with 401 GitHub stars.
OpenRCA is primarily written in Python. It covers topics such as benchmark, large-language-models, llm.
You can find installation instructions and usage details in the OpenRCA GitHub repository at github.com/microsoft/OpenRCA. The project has 401 stars and 51 forks, indicating an active community.
OpenRCA is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to OpenRCA on Agent Skills Hub include promptdesk, LLMCompiler, ongrid. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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