industry-4-predictive-maintenance
Reusable Windsurf rules and Cursor agent skill files for cross-project AI-assisted development
Install / Use
npx skills add Ryukijano/agent-skills --skill industry-4-predictive-maintenanceInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
Our assessment of industry-4-predictive-maintenance
industry-4-predictive-maintenance scores 53/100 on our quality scale, 4252nd of 4,625 Development & Engineering skills we index.
Its SKILL.md is 1.4 KB long, well organised into 8 sections with 1 code example: moderately detailed.
It has no GitHub stars yet, so there is no community track record; judge it on its content.
Maintenance, license and trust
- We could not determine when the repository was last updated.
- Our last check on 2026-09-06 found the source still online.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 68/100, with 3 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-10-27. Automated pattern scan on 2026-10-26. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
industry-4-predictive-maintenance compared with similar skills
All 4 of these similar skills score higher than industry-4-predictive-maintenance; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| industry-4-predictive-maintenance (this skill)by Ryukijano | 53 | 0 | — | SKILL.md |
| Agent-Reachby Panniantong | 100 | 103.9k | 2d ago | CLAUDE.md |
| taste-skillby Leonxlnx | 100 | 103.1k | today | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 77.9k | today | CLAUDE.md |
| ai-job-searchby ai-job-search-dev | 100 | 66.4k | today | CLAUDE.md |
Frequently asked questions
- How do I install industry-4-predictive-maintenance?
- Run
npx skills add Ryukijano/agent-skills --skill industry-4-predictive-maintenance. The install tabs above show the steps for each supported agent. - Which AI agents does industry-4-predictive-maintenance work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is industry-4-predictive-maintenance safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It declares no license and scores 68/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is industry-4-predictive-maintenance still maintained?
- We could not determine when the repository was last updated.
Skill content
View source on GitHubIndustry 4.0, Predictive Maintenance, and Digital Twins
Description
RAPIDS, NVIDIA Omniverse, XGBoost, anomaly detection, and digital twins for manufacturing.
When to use
You are applying ML to manufacturing: predictive maintenance, defect detection, process optimization.
Key concepts
- Predictive maintenance: RUL estimation, anomaly detection, vibration/sensor data.
- Defect detection: computer vision for quality control.
- Digital twins: NVIDIA Omniverse, OpenUSD, PEGAVERSE for factory simulation.
- Time-series: LSTM, TCN, transformers for sensor data.
Code pattern
import xgboost as xgb
import cudf
X = cudf.read_csv("sensors.csv")
model = xgb.XGBRegressor(tree_method="hist", device="cuda")
model.fit(X, y)
Tuning notes
- Use imbalanced learning techniques for rare failures.
- Digital twins need CAD/3D models and real-time sensor feeds.
- Combine physics-based degradation models with ML.
Verification
- Train an RUL model and evaluate on a held-out test set.
- Run anomaly detection on sensor data and compare to known failures.
- Build a small digital twin and verify it mirrors real process behavior.
References
- https://developer.nvidia.com/blog/accelerating-predictive-maintenance-in-manufacturing-with-rapids-ai/
- https://developer.nvidia.com/blog/pegatron-simulates-and-optimizes-factory-operations-with-ai-enabled-digital-twins/
- https://www.mdpi.com/2076-3417/15/6/3166
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.