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Supply Chain & Logistics Intelligencel

Name: Supply Chain & Logistics Intelligencel
Author: ai-gaoqian

作者 ai-gaoqian · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ✓ 安全检测通过

总下载

当前安装

版本数

在 OpenClaw 中安装

/install supply-chain-intel

功能描述

AI-powered global supply chain and logistics intelligence engine. Tracks ocean freight rates (Drewry/Freightos), port throughput (LA/LB, China ports), trade...

使用说明 (SKILL.md)

Supply Chain & Logistics Intelligence

Capabilities

#	Capability	Input	Output
1	Freight Rate Dashboard	Route (e.g., Shanghai-LA) / mode (ocean/air)	Spot rate, 1Y range, trend, capacity outlook, booking lead time
2	Port Congestion Monitor	Port(s) / region	Vessel queue length, dwell time, gate hours, labor status, weather impact
3	Trade Flow Analyzer	Country pair / commodity (HS code)	Volume, value, growth rate, seasonality, tariff impact, alternative routes
4	Commodity Bottleneck Scanner	Commodity (semiconductors, batteries, etc)	Key suppliers, geographic concentration, lead time, price volatility, substitution options
5	Supply Chain Risk Heatmap	Company / product / region	Geopolitical risk, climate exposure, labor disruption probability, regulatory compliance burden
6	Transit Time Estimator	Origin-destination + mode	Current transit days, historical variability, delay probability, expedited options cost
7	Inventory Optimization Model	Demand forecast + lead time variability	Safety stock level, reorder point, EOQ, service level vs. carrying cost trade-off
8	Sourcing Intelligence	Component / raw material	Supplier landscape, pricing benchmarks, quality ratings, ESG compliance, dual-sourcing feasibility
9	Logistics Cost Benchmark	Shipment profile (weight, volume, value)	Cost breakdown (freight, fuel surcharge, customs, insurance), vs. industry average
10	Disruption Alert System	Watchlist (ports, suppliers, routes)	Real-time alerts (strikes, weather, sanctions), impact assessment, contingency plan suggestions

Workflow

User Query
  │
  ├─ [Step 1] Classify → logistics mode + commodity + geography + time horizon
  │
  ├─ [Step 2] Multi-source data retrieval:
  │   └─ Freight rates: Drewry, Freightos
  │   └─ Port data: Port of LA/LB, China Ports Association
  │   └─ Trade: UN Comtrade, US Census
  │   └─ Risk: Resilinc, Bloomberg SCM
  │   └─ Equipment: Container xChange
  │
  ├─ [Step 3] Cross-validate & flag discrepancies
  │
  ├─ [Step 4] Apply supply chain models:
  │   └─ Inventory optimization (EOQ, safety stock)
  │   └─ Network design (facility location, routing)
  │   └─ Risk quantification (VaR for lead time)
  │
  ├─ [Step 5] Generate structured output with actionable insights
  │
  └─ [Step 6] Cite data vintage, source URLs, confidence intervals

Output Formats

Freight Rate Snapshot

Route	Mode	Spot Rate	1W Change	1Y Range	Capacity	Booking Lead Time
Shanghai-LA	Ocean	$X,XXX/TEU	+X%	$X,XXX-$X,XXX	Tight	3-4 weeks
Frankfurt-ORD	Air	$X.XX/kg	-X%	$X.XX-$X.XX	Available	1-2 days

Port Congestion Dashboard

Port	Vessels Waiting	Avg Dwell Time (days)	Gate Hours	Labor Status	Weather Alert
Los Angeles	12	4.2	24/7	Normal	None
Rotterdam	8	3.8	6am-10pm	Strike warning	High winds

Commodity Bottleneck Matrix

Commodity	Key Suppliers	Geographic Risk	Lead Time (weeks)	Price Volatility	Substitution Options
Advanced Semiconductors	TSMC, Samsung, Intel	Taiwan Strait, US-China	26-52	High	None (critical)
Lithium-ion Batteries	CATL, LG, Panasonic	China, DRC, Chile	12-24	Medium	Sodium-ion (emerging)

Usage Guidelines

Real-time data priority — supply chain data decays rapidly; flag any data >7 days old
Multi-modal comparison — always present air vs. ocean vs. rail trade-offs (cost vs. speed vs. reliability)
Risk quantification — express disruptions in $ impact and lead time extension, not just qualitative
Actionable recommendations — each insight should link to a decision (reroute, expedite, buffer stock, dual-source)
Regulatory compliance — include customs, sanctions (OFAC), forced labor (UFLPA), carbon border (CBAM) considerations
Scenario planning — provide best-case/worst-case/base-case for critical decisions

Examples

Example 1: Freight Cost Optimization

User: "Best way to ship 100 TEU from Shenzhen to Chicago in Q3 2026?" Output: Ocean vs. rail vs. air cost/speed comparison; port pair recommendations (Shenzhen→LA vs. Shenzhen→Vancouver); transit time variability; fuel surcharge forecast; contingency for Panama Canal drought.

Example 2: Disruption Impact Assessment

User: "What's the impact of a potential ILWU strike at LA/LB ports?" Output: Historical strike duration (days), backlog buildup rate (TEU/day), alternative ports (Oakland, Tacoma, Mexico), cost premium for air freight, inventory burn-down timeline for key industries.

Example 3: Sourcing Strategy

User: "Should we dual-source rare earth magnets from China and Vietnam?" Output: Supplier capability comparison, quality variance, lead time differential, tariff implications, ESG risk (China Xinjiang concerns), total landed cost model.

Data Base: references/supply_chain_sources.json — 14 authoritative data sources, 5 key commodities, 5 risk factors, 4 logistics modes. Last Updated: June 2026 Free Tier: Available. This skill aggregates public supply chain data; no proprietary carrier contracts accessed.

安全使用建议

This skill appears safe to install for logistics research. Users should be aware that it may activate on broad business queries and that some listed data sources may require separate access or licensing; verify freshness and source permissions before relying on operational recommendations.

能力评估

✓ Purpose & Capability

The stated purpose is supply-chain and logistics intelligence, and the artifacts contain guidance for freight, ports, trade flows, risk, sourcing, and inventory analysis consistent with that purpose.

ℹ Instruction Scope

The trigger list includes some broad business terms such as inventory management, customs clearance, and warehouse location, but the runtime instructions remain logistics-focused and do not add hidden or unrelated behavior.

✓ Install Mechanism

The package contains only SKILL.md and a JSON reference file; no executable scripts, dependency installs, or setup commands are present.

✓ Credentials

The skill expects retrieval from named public or commercial logistics information sources, which is proportionate to its research purpose and does not request local file, credential, browser-session, or account access.

✓ Persistence & Privilege

No persistence, background workers, privilege escalation, destructive operations, or mutation authority are described in the artifacts.

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 supply-chain-intel
安装完成后，直接呼叫该 Skill 的名称或使用 /supply-chain-intel 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

v1.0.0

Initial release of Supply Chain & Logistics Intelligence: - Provides AI-powered analysis of global supply chain and logistics data, including freight rates, port throughput, trade flows, and commodity bottlenecks. - Monitors 14 key risk factors and supports 4 main logistics modes (air, ocean, rail, truck). - Delivers real-time disruption alerts and cost optimization insights. - Includes dashboards for port congestion, commodity supply risk, and freight benchmarking. - Offers actionable recommendations with scenario planning, regulatory compliance, and risk quantification. - Covers 14 authoritative data sources and 5 major commodity bottlenecks, accessible via public data aggregation.

元数据

Slug supply-chain-intel

版本 1.0.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

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