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Agent Skill · 前端设计

warehouse-optimization(仓库优化)

warehouse-optimization 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

warehouse-optimization 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 3,701。

总安装 1,164周安装 50GitHub Stars 131下载 408核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:warehouse-optimization(仓库优化)
来源仓库:https://github.com/nexscope-ai/ecommerce-skills
仓库路径:skills/warehouse-optimization
安装命令:
npx skills add https://github.com/nexscope-ai/ecommerce-skills --skill warehouse-optimization
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/nexscope-ai/ecommerce-skills --skill warehouse-optimization

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

该技能用于优化仓储管理与库存配置,提升物流效率与成本控制能力。适用于电商仓库规划、SKU 分类优化及订单履约流程设计。提供货架布局建议、拣货路径优化与库存周转率分析工具。需接入仓库管理系统(WMS)API 或导入 CSV 库存数据进行计算。输出结果应结合实际物理空间与作业流程进行人工复核调整。

来源仓库内路径:skills/warehouse-optimization

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/nexscope-ai/ecommerce-skills --skill warehouse…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

warehouse-optimization 怎么安装?

在终端执行 npx skills add https://github.com/nexscope-ai/ecommerce-skills --skill warehouse-optimizat…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

warehouse-optimization 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

warehouse-optimization 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

warehouse-optimization 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

warehouse-optimization 用的人多吗?

公开数据是累计安装 1,164 次,近一周安装 50 次,下载 408 次,GitHub 131 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

warehouse-optimization 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
warehouse-optimization

仓库优化 · 本页

1,164131接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Warehouse & Inventory Optimization 🏭

Diagnose and optimize your warehouse operations: analyze inventory health, calculate safety stock, reduce costs, and improve efficiency. No API key required.

Installation

npx skills add nexscope-ai/eCommerce-Skills --skill warehouse-optimization -g

Supported Fulfillment Models

ModelPlatformOptimization Focus
Self-FulfillmentAnyWarehouse layout, staffing, pick/pack efficiency, storage costs
Amazon FBAAmazonIPI score, storage fees, aged inventory, restock limits
Amazon FBMAmazonShipping speed, Prime eligibility, cost vs FBA
Walmart WFSWalmartFulfillment fees, storage limits, Pro Seller status
3PLMulti-channelProvider costs, SLAs, contract optimization, hidden fees
Shopify Fulfillment NetworkShopifyDistributed inventory, delivery speed, cost analysis
TikTok Shop FulfillmentTikTokTikTok-specific requirements, shipping SLAs
DropshippingAnySupplier reliability, lead times, stockout prevention
HybridMulti-channelInventory allocation, channel balancing, split strategy

Usage Examples

Audit my warehouse operations. I'm self-fulfilling from a 2,000 sq ft warehouse.
500 SKUs, 3,000 orders/month. Main issues: frequent stockouts on top sellers,
high storage costs on slow movers. Help me optimize.
I use FBA for my Amazon store. IPI score dropped to 350. I have excess inventory
warnings on 40 SKUs. How do I fix this before I get storage limits?
Running FBM for my oversized products and FBA for standard. 200 orders/day total.
Which SKUs should I move to FBA vs keep FBM? Help me optimize the split.
Using ShipBob as my 3PL. Monthly bill is $8,500 for 2,000 orders. Is this competitive?
What should I negotiate or consider switching?

First Interaction

When user first asks about warehouse optimization, inventory management, or fulfillment efficiency, greet them with:

🏭 Warehouse Optimization ready!

I'll help you diagnose issues and optimize your inventory operations.

**Tell me about your setup:**
- Fulfillment model (FBA, FBM, 3PL, self-fulfill, hybrid?)
- Approximate SKU count
- Monthly order volume
- Main pain points (stockouts, high costs, slow shipping, IPI issues?)

Or just describe your situation and I'll guide you from there.

Handling Incomplete Input

To optimize your warehouse operations, I need:

**Required:**
- Fulfillment model: Self / FBA / FBM / WFS / 3PL / Dropship / Hybrid
- Approximate SKU count
- Monthly order volume
- Main pain points (stockouts, high costs, slow shipping, etc.)

**Recommended (deeper analysis):**
- Top 10 SKUs by sales volume (or % of total sales)
- Current inventory turnover rate (if known)
- Average days of inventory on hand
- Monthly storage/fulfillment costs
- For FBA: Current IPI score, aged inventory alerts
- For 3PL: Current provider and monthly costs

Audit Workflow

Step 1: Collect Current State Data

Data PointWhy It Matters
Fulfillment modelDetermines optimization approach
SKU countComplexity indicator
Monthly ordersScale of operations
Top SKUs (% of sales)For ABC analysis
Current turnover rateInventory health indicator
Days of inventoryOver/understock signal
Stockout frequencyLost sales indicator
Storage costsCost optimization potential
Pick/pack accuracyQuality indicator

Step 2: Calculate Key Metrics

Inventory Turnover Rate:

Inventory Turnover = Cost of Goods Sold (COGS) / Average Inventory Value
  • Benchmark: 4-6x/year for most e-commerce (higher = better)
  • Low turnover (<4): Excess inventory, capital tied up
  • High turnover (>8): Risk of stockouts, tight supply chain

Days of Inventory (DOI):

DOI = (Average Inventory / COGS) × 365
  • Target: 30-60 days for most products
  • Too high (>90 days): Overstock, storage cost drain
  • Too low (<14 days): Stockout risk

Stockout Rate:

Stockout Rate = (Days Out of Stock / Total Days) × 100
  • Target: <2%
  • Impact: Each 1% stockout ≈ 1% lost revenue

Perfect Order Rate:

Perfect Order Rate = (Orders Shipped Complete, On-Time, Undamaged / Total Orders) × 100
  • Target: >95%

Step 3: Perform ABC Analysis

Classify SKUs by revenue contribution:

Class% of SKUs% of RevenueInventory Strategy
A~20%~80%High priority, never stockout, frequent replenishment
B~30%~15%Moderate priority, standard replenishment
C~50%~5%Low priority, review for discontinuation

Recommendations by class:

  • A items: Safety stock = 2-4 weeks, reorder frequently, prime warehouse locations
  • B items: Safety stock = 2-3 weeks, standard locations
  • C items: Minimal safety stock, consider dropship or discontinue slow movers

Step 4: Calculate Safety Stock & Reorder Points

Safety Stock Formula:

Safety Stock = Z × σd × √L

Where:
- Z = Service level factor (1.65 for 95%, 2.33 for 99%)
- σd = Standard deviation of daily demand
- L = Lead time in days

Simplified Safety Stock (if limited data):

Safety Stock = (Max Daily Sales - Avg Daily Sales) × Lead Time

Reorder Point Formula:

Reorder Point = (Avg Daily Sales × Lead Time) + Safety Stock

Example calculation:

Product: Widget A
- Average daily sales: 10 units
- Max daily sales: 18 units
- Lead time: 14 days

Safety Stock = (18 - 10) × 14 = 112 units
Reorder Point = (10 × 14) + 112 = 252 units

→ Reorder when inventory hits 252 units
→ Keep 112 units as buffer

Step 5: Analyze Costs

Fulfillment Cost Benchmarks:

Cost ComponentSelf-Fulfill3PLFBA
Storage$0.30-0.50/cu ft$0.45-0.75/cu ft$0.87-2.40/cu ft
Pick & PackLabor-based$1.50-3.00/orderIncluded in fee
ShippingCarrier ratesDiscounted ratesPrime rates
ReturnsLabor + space$3-8/returnFree for buyers

Cost Per Order (CPO):

CPO = (Storage + Labor + Packaging + Shipping) / Total Orders

Inventory Carrying Cost:

Carrying Cost = Average Inventory Value × Carrying Rate (typically 20-30%/year)

Includes: Storage, insurance, obsolescence, opportunity cost

Step 6: Platform-Specific Analysis

Amazon FBA:

  • IPI Score factors: Excess inventory %, sell-through rate, stranded inventory, in-stock rate
  • Storage fee triggers: Aged inventory (181+ days), low IPI (<400)
  • Restock limits: Based on IPI and sales velocity

Amazon FBM:

  • Prime eligibility: Seller Fulfilled Prime requirements
  • Shipping performance: On-time delivery, valid tracking rate
  • Cost comparison: When FBM beats FBA (oversized, slow movers)

Walmart WFS:

  • Pro Seller badge: Fulfillment performance requirements
  • Storage fees: Generally lower than FBA
  • Limitations: Product restrictions, geographic coverage

3PL Providers:

  • Contract terms: Minimum commitments, peak surcharges
  • Hidden costs: Receiving fees, special handling, return processing
  • Performance SLAs: Shipping accuracy, turnaround time

Step 7: Generate Recommendations

Prioritize by impact and effort:

## Recommendations

### 🔴 Critical (Do Now)
| Issue | Impact | Action | Expected Result |
|-------|--------|--------|-----------------|

### 🟡 Important (This Month)
| Issue | Impact | Action | Expected Result |
|-------|--------|--------|-----------------|

### 🟢 Optimization (This Quarter)
| Issue | Impact | Action | Expected Result |
|-------|--------|--------|-----------------|

FBA-Specific Optimization

IPI Score Improvement

FactorTargetActions
Excess inventory<5%Create removal orders, run promotions, liquidate
Sell-through rate>4.5Improve listing, PPC, reduce price
Stranded inventory0%Fix listing errors, match ASINs
In-stock rate>90%Increase replenishment frequency

Aged Inventory Prevention:

  • Monitor inventory age weekly
  • Take action before 181 days (aged fee trigger)
  • Options: Removal order, outlet deals, liquidation, donate

Storage Fee Calendar:

  • Jan-Sep: Standard rates
  • Oct-Dec: Peak rates (3x higher)
  • Aged inventory surcharge: 181+ days

FBA Restock Calculation

Target FBA Inventory = (Avg Daily Units × Days of Cover) + Safety Buffer

Where:
- Days of Cover: 30-60 days (varies by IPI score)
- Safety Buffer: 1-2 weeks for top sellers

Example:
- Selling 10 units/day
- Target 45 days cover
- Safety: 10 days

Target = (10 × 45) + (10 × 10) = 550 units

3PL Cost Optimization

Evaluate Your 3PL Costs

Cost TypeWhat to Check
StoragePer pallet vs per cu ft, minimum charges
Pick & PackPer order vs per item, kit fees
ReceivingPer unit, per carton, or per shipment
Special handlingFragile, hazmat, temperature-controlled
Peak surchargesQ4 rate increases
Minimum commitmentsMonthly minimums, long-term contracts

3PL Benchmark Costs (2025)

ServiceLowAverageHigh
Storage (per pallet/mo)$8$15$25
Pick & Pack (per order)$2.50$4.00$6.00
Additional item$0.30$0.75$1.50
Receiving (per unit)$0.20$0.40$0.75

When to Switch 3PLs

  • Cost per order >20% above benchmark
  • SLA failures >5% of orders
  • Poor communication / slow issue resolution
  • No volume-based discounts after 6+ months
  • Geographic mismatch (shipping zones too far)

Output Format

# 🏭 Warehouse Optimization Report

**Business:** [Business Name/Type]
**Fulfillment Model:** [Self / FBA / FBM / WFS / 3PL / Hybrid]
**Analysis Date:** [Date]

---

## 1. Current State Summary

| Metric | Current | Benchmark | Status |
|--------|---------|-----------|--------|
| Monthly orders | X | — | — |
| SKU count | X | — | — |
| Inventory turnover | Xx/year | 4-6x | 🟢/🟡/🔴 |
| Days of inventory | X days | 30-60 | 🟢/🟡/🔴 |
| Stockout rate | X% | <2% | 🟢/🟡/🔴 |
| Cost per order | $X | $3-8 | 🟢/🟡/🔴 |

---

## 2. ABC Analysis

[SKU classification table]

---

## 3. Inventory Optimization

### Safety Stock Recommendations
| SKU/Class | Current Stock | Recommended Safety Stock | Reorder Point |
|-----------|---------------|--------------------------|---------------|

### Overstock Alert
[SKUs with excess inventory]

### Stockout Risk
[SKUs at risk of stockout]

---

## 4. Cost Analysis

[Cost breakdown and optimization opportunities]

---

## 5. Recommendations

### 🔴 Critical
[High-impact, do now]

### 🟡 Important
[Medium-impact, this month]

### 🟢 Optimization
[Lower priority improvements]

---

## 6. Expected Results

| Improvement Area | Current | Target | Timeline |
|------------------|---------|--------|----------|
| Stockout rate | X% | X% | X weeks |
| Storage costs | $X | $X | X months |
| Turnover rate | Xx | Xx | X months |

Calculations Reference

Core Formulas

MetricFormula
Inventory TurnoverCOGS / Average Inventory
Days of Inventory(Avg Inventory / COGS) × 365
Safety StockZ × σd × √Lead Time
Reorder Point(Avg Daily Sales × Lead Time) + Safety Stock
Carrying CostAvg Inventory × 25% (typical rate)
Cost Per OrderTotal Fulfillment Costs / Total Orders

Service Level Factors (Z)

Service LevelZ Factor
90%1.28
95%1.65
97.5%1.96
99%2.33

Limitations

This skill provides strategic analysis and calculations based on industry benchmarks and user-provided data. It cannot access real-time inventory systems, WMS data, or marketplace APIs. For integrated inventory management, check out Nexscope — Your AI Assistant for smarter E-commerce decisions.


Related Skills

  • Amazon Skills — Keyword research, listing optimization, PPC campaigns, sales estimation
  • eCommerce Skills — Cross-platform tools for all e-commerce businesses

Built by Nexscope — research, validate, and act on e-commerce opportunities with AI.

平台分布

Codex

34.5%
按下载量换算141

Claude

30.29%
按下载量换算124

Cursor

17.76%
按下载量换算72

Gemini CLI

8.71%
按下载量换算36

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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