Skill: jaccen/fde-industrial-skill (version 2.0.1)

--- name: fde-industrial-skill description: "FDE skill for industrial AI deployment: scenario diagnosis, data governance, solution design, POC-to-scale methodology, ROI quantification. Covers predictive maintenance, visual inspection, process optimization, energy efficiency, supply chain. Triggers: FDE, industrial AI, smart manufacturing, factory AI, AI deployment." version: 2.0.1 author: jaccen tags: [FDE, industrial-ai, smart-manufacturing, predictive-maintenance, visual-inspection] trigger: FDE, forward deployed engineer, industrial AI, smart manufacturing, AI deployment, production line AI, factory AI, predictive maintenance, visual inspection, industrial big data, AI+manufacturing --- # FDE Industrial AI Deployment Skill > Open Source: https://github.com/jaccen/FDE-Industrial-Skill ## Overview Full-spectrum support for FDEs deploying AI & big data on industrial production lines — from scenario diagnosis to scaled deployment. ## Core Workflow ``` Scenario Diagnosis -> Data Governance -> Solution Design -> POC -> Scale-up -> Feedback Loop ``` ### Step 1: Scenario Diagnosis 1. Read [references/fde-role-model.md](references/fde-role-model.md) for FDE capability framework. 2. Apply "Pain-Data-Impact" triage: Pain (business pain), Data (sufficiency), Impact (quantifiable ROI). 3. Classify into 5 core categories — [references/industrial-ai-scenarios.md](references/industrial-ai-scenarios.md). ### Step 2: Data Governance & Integration 1. Map data sources: OT (SCADA/PLC/sensors), IT (MES/ERP/PLM), ET (engineering docs). 2. Palantir-style Ontology: Objects, Links, Actions. 3. Data quality gaps: missing values, timestamp misalignment, label scarcity. 4. Pipeline: edge collection -> ETL -> feature store. **Key**: Start from business decisions, not data tables. ### Step 3: Solution Design - **Visual inspection**: CNN/ViT + edge GPU boxes - **Predictive maintenance**: LSTM/Transformer + physics-informed features; 7-14 day window - **Process optimization**: RL/Bayesian + digital twin; single process first - **Energy efficiency**: regression + control optimization; baseline first - **Supply chain**: graph model + demand forecast + ERP integration ### Step 4: POC Deployment (Zero Week) Day 1-3: data audit + interviews; Day 4-7: baseline model + quick wins; Week 2-4: training + integration; Week 4-6: A/B test + operator training. **Critical**: Deliver measurable quick win within 2 weeks. ### Step 5: Scale-up & Feedback Measure ROI, generalize single -> multi -> factory-wide, FDE+FDR feedback loop. ## ROI Framework | Metric | Typical Range | |--------|--------------| | Defect detection improvement | 80-95% reduction | | Unplanned downtime reduction | 30-60% reduction | | Yield improvement | 2-8% increase | | Energy savings | 5-15% reduction | | ROI payback period | 6-18 months | ## Reference Guide | Need | Reference | |------|-----------| | FDE role & skills | [fde-role-model.md](references/fde-role-model.md) | | Scenario & algorithm | [industrial-ai-scenarios.md](references/industrial-ai-scenarios.md) | | Deployment methodology | [landing-methodology.md](references/landing-methodology.md) | | Case studies | [case-studies.md](references/case-studies.md) |


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