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在 OpenClaw 中安装
/install fde-industrial-skill
功能描述
FDE skill for industrial AI deployment: scenario diagnosis, data governance, solution design, POC-to-scale methodology, ROI quantification. Covers predictive...
使用说明 (SKILL.md)
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
- Read references/fde-role-model.md for FDE capability framework.
- Apply "Pain-Data-Impact" triage: Pain (business pain), Data (sufficiency), Impact (quantifiable ROI).
- Classify into 5 core categories — references/industrial-ai-scenarios.md.
Step 2: Data Governance & Integration
- Map data sources: OT (SCADA/PLC/sensors), IT (MES/ERP/PLM), ET (engineering docs).
- Palantir-style Ontology: Objects, Links, Actions.
- Data quality gaps: missing values, timestamp misalignment, label scarcity.
- 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 |
| Scenario & algorithm | industrial-ai-scenarios.md |
| Deployment methodology | landing-methodology.md |
| Case studies | case-studies.md |
安全使用建议
Treat this as an incomplete review: install only after independently checking metadata.json and the artifact directory for broad permissions, credential handling, persistence, or hidden execution.
能力标签
能力评估
Purpose & Capability
Artifact inspection was attempted, but local file reads failed before metadata.json or artifact contents could be reviewed.
Instruction Scope
No instruction-scope risk is reported because no artifact text could be inspected.
Install Mechanism
No install-mechanism risk is reported because install artifacts could not be read.
Credentials
No environment-proportionality risk is reported because capability and runtime artifacts could not be inspected.
Persistence & Privilege
No persistence or privilege risk is reported because the relevant artifacts could not be inspected.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install fde-industrial-skill - 安装完成后，直接呼叫该 Skill 的名称或使用
/fde-industrial-skill触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v2.0.1
- Updated GitHub repository link in documentation to https://github.com/jaccen/FDE-Industrial-Skill.
- Incremented version to 2.0.1.
v2.0.0
Major update with ecosystem content removal and streamlined focus.
- Removed China Telecom AI ecosystem details from documentation.
- Updated tags to exclude China Telecom-specific terms.
- Cleaned description to focus on core industrial AI skills and methodology.
- Improved clarity and conciseness across documentation sections.
v1.1.3
- Updated skill name casing in metadata from "FDE-Industrial-Skill" to "fde-industrial-skill".
- Incremented version from 1.1.2 to 1.1.3.
- No changes made to the documentation content or features.
v1.1.2
- Expanded and clarified documentation in SKILL.md for end-to-end industrial AI deployment.
- Included detailed scenario workflow, data governance guidance, and solution design strategies.
- Added specific industrial use cases: predictive maintenance, visual inspection, process optimization, energy efficiency, and supply chain.
- Integrated China Telecom AI ecosystem overview and tools relevant for FDEs.
- Provided ROI metrics and reference guide for quick access to core resources.
元数据
常见问题
Clawhub Publish 是什么？
FDE skill for industrial AI deployment: scenario diagnosis, data governance, solution design, POC-to-scale methodology, ROI quantification. Covers predictive... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 147 次。
如何安装 Clawhub Publish？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install fde-industrial-skill」即可一键安装，无需额外配置。
Clawhub Publish 是免费的吗？
是的，Clawhub Publish 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Clawhub Publish 支持哪些平台？
Clawhub Publish 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Clawhub Publish？
由 jaccen（@jaccen）开发并维护，当前版本 v2.0.1。
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