SkillCheatSheet
detecting-anomalies-in-industrial-control-systems
An AI agent skill from mukul975/Anthropic-Cybersecurity-Skills, scored 57 out of 100 by code.
Code score by group
It loses the most points in Procedure (15), the group that checks whether the steps are numbered, read the context first and say what to do when something fails.
What the author says it does
Deploys anomaly detection for OT/ICS environments using machine learning on OT network baselines, physics-based process models, and Modbus/DNP3/OPC UA traffic analysis to flag deviations, rogue devices, and mismatches against historian data. Use for continuous OT monitoring, baselining deterministic SCADA polling, or investigating alerts from Nozomi Guardian/Dragos needing deeper protocol analysis.
This is the description in the skill's own SKILL.md, written by its author, not by SkillCheatSheet.
- Repository
- mukul975/Anthropic-Cybersecurity-Skills
- Folder
- skills/detecting-anomalies-in-industrial-control-systems
- License
- Apache-2.0
- GitHub stars
- 35,958
- SKILL.md lines
- 333
- Other files
- 3
Add detecting-anomalies-in-industrial-control-systems to your agent
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-anomalies-in-industrial-control-systemsThe command uses the open skills CLI. A skill is instructions and scripts your agent will follow, so read it first: open the folder on GitHub at the commit that was scored.
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All skills in mukul975/Anthropic-Cybersecurity-Skills
Scores come from a snapshot of 300 public GitHub repositories taken on 12 November 2026. Every score is the result of fixed checks run by code; no AI is involved. How scoring works.