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ecom-inventory-health电子商务库存健康状况

Agent Skill

ecom-inventory-health 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。

总安装

349

周安装

15

GitHub Stars

125

下载量

122
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:ecom-inventory-health(电子商务库存健康状况)
来源仓库:https://github.com/asgard-ai-platform/skills
仓库路径:skills/ecom-inventory-health
安装命令:
npx skills add https://github.com/asgard-ai-platform/skills --skill ecom-inventory-health
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/asgard-ai-platform/skills --skill ecom-inventory-health

简介

库存健康度分析工具,平衡缺货与滞销风险,提供 ABC 分类与周转率监控。

  • 适用于 SKU 级库存优化,重点关注 top 20% 高价值商品的精细管控。
  • 核心指标包括周转天数、服务水平与滞销预警,支持动态补货策略制定。
  • 需结合历史销售数据与供应链节奏,避免过度依赖单一指标做决策。
  • ecom-inventory-health 属于待分类类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Inventory Health Analysis

Overview

Inventory health balances two risks: stockouts (lost sales, unhappy customers) and overstock (carrying costs, obsolescence). This skill provides tools to measure, classify, and optimize inventory levels.

Framework

IRON LAW: Not All SKUs Deserve Equal Attention

ABC classification shows that ~20% of SKUs drive ~80% of revenue.
Treat A-items (top 20% revenue) with tight control and frequent review.
C-items (bottom 50% revenue) get simple rules and less attention.
Equal treatment of all SKUs wastes resources on low-impact items.

Key Metrics

MetricFormulaHealthy Range
Inventory TurnoverCOGS / Avg Inventory4-12x/year (industry-dependent)
Days of Inventory (DOI)365 / Inventory Turnover30-90 days
Stockout RateStockout incidents / Total demand occasions< 2-5%
Fill RateOrders filled completely / Total orders> 95%
Carrying CostAvg Inventory × Carrying Cost % (typically 20-30%/year)Minimize
Dead Stock %Items with zero sales in 6+ months / Total SKUs< 10%

ABC Classification

ClassRevenue %SKU %Strategy
A~80%~20%Tight control, frequent review, safety stock optimized
B~15%~30%Moderate control, periodic review
C~5%~50%Simple rules, min/max levels, consider dropping

Safety Stock Calculation

Safety Stock = Z × σ_d × √(Lead Time)

Where:
- Z = service level factor (1.65 for 95%, 2.33 for 99%)
- σ_d = standard deviation of daily demand
- Lead Time = supplier lead time in days

Reorder Point

Reorder Point = (Average Daily Demand × Lead Time) + Safety Stock

Diagnosis Steps

Phase 1: Overall Health Check

  • Calculate turnover and DOI for total inventory
  • Compare to industry benchmarks
  • Identify trend: improving or deteriorating?

Phase 2: ABC Classification

  • Rank all SKUs by revenue contribution
  • Classify into A/B/C
  • Check: are A-items well-stocked? Are C-items over-stocked?

Phase 3: Problem Identification

  • Overstock: DOI > 90 days, dead stock > 10%, carrying costs rising
  • Stockout: Fill rate < 95%, lost sales reports, customer complaints
  • Imbalance: A-items understocked while C-items overstocked

Phase 4: Optimization

  • Set safety stock by ABC class
  • Implement reorder points for A-items
  • Liquidate dead stock (discount, bundle, donate)
  • Reduce lead times through supplier negotiation

Output Format

# Inventory Health Report: {Business}

## Summary
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Turnover | {X}x | {X}x | 🟢/🟡/🔴 |
| DOI | {X} days | {X} days | 🟢/🟡/🔴 |
| Fill Rate | {X%} | >95% | 🟢/🟡/🔴 |
| Dead Stock | {X%} | <10% | 🟢/🟡/🔴 |

## ABC Distribution
| Class | SKUs | Revenue % | Avg DOI | Issue |
|-------|------|----------|---------|-------|
| A | {N} | {%} | {days} | {stockout risk?} |
| B | {N} | {%} | {days} | ... |
| C | {N} | {%} | {days} | {overstock?} |

## Top Issues
1. {issue with specific SKUs and data}

## Recommendations
1. {action with expected impact}

Gotchas

  • Seasonal products need separate treatment: Swimsuits in January will show as "dead stock" but shouldn't be liquidated. Use seasonal adjustment or analyze by season.
  • Inventory turnover varies hugely by industry: Grocery: 20-50x/year. Fashion: 4-6x. Electronics: 6-12x. Always benchmark within industry.
  • Low turnover ≠ bad if intentional: Strategic inventory (buying ahead of price increases, securing supply) may justify lower turnover.
  • ABC classifications shift: A product that was A-class last year may be C-class this year. Reclassify quarterly.
  • Carrying cost is often underestimated: Include: warehouse rent, insurance, obsolescence, capital cost (opportunity cost of money tied up), handling labor. Total is typically 20-30% of inventory value per year.

References

  • For EOQ (Economic Order Quantity) model, see references/eoq-model.md
  • For seasonal demand forecasting, see references/seasonal-forecasting.md

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

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能力 2

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能力 3

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能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.78%
按下载量换算44

Claude

28.1%
按下载量换算34

Cursor

18.26%
按下载量换算22

Gemini CLI

8.19%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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