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assortment-scout品类侦察员

Agent Skill

assortment-scout 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

2,791

周安装

114

GitHub Stars

公开资料未说明

下载量

894
OpenClaw

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

MIT-0

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:assortment-scout(品类侦察员)
来源仓库:https://github.com/harrylabsj/assortment-scout
安装命令:
openclaw skills install assortment-scout
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install assortment-scout

简介

审核电子商务目录,发现 SKU 蔓延、价格覆盖差距等问题并处理。

  • 适用于 OpenClaw 中的品类管理与电商运营优化场景。
  • 识别英雄依赖、长尾膨胀等风险,支持粗暴处理以提升效率。
  • 安装前需确认权限范围、维护状态及是否触发数据读写或外部服务调用。
  • assortment-scout 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
assortment-scout
description
Audit an ecommerce catalog, spot SKU sprawl, price and attribute coverage gaps, hero dependence, long-tail bloat, and duplicate-risk clusters, then turn rough catalog notes or CSV exports into keep-add-expand-merge-retire recommendations and a 30-day merchandising brief. Use when merchandisers, category managers, marketplace sellers, or consultants need assortment planning support without live ERP, PIM, or marketplace APIs.

Assortment Scout

Overview

Use this skill to turn catalog notes, export summaries, and merchandising goals into a practical assortment review. It is built for operators who need a fast decision layer for what to keep, expand, bundle, merge, or retire.

This MVP is heuristic. It does not access live Shopify, Amazon, ERP, PIM, or marketplace systems. It relies on the user's provided catalog structure, product performance notes, and business constraints.

Trigger

Use this skill when the user wants to:

  • reduce SKU clutter or long-tail bloat
  • identify price-band, feature, or variant coverage gaps
  • review duplicate-risk or cannibalization concerns
  • prepare a category review, seasonal line review, or catalog cleanup memo
  • turn pasted catalog notes into a prioritized merchandising action brief

Example prompts

  • "Audit our catalog for SKU clutter and hero-product dependence"
  • "Find assortment gaps across our travel accessories line"
  • "Which products should we keep, merge, bundle, or retire?"
  • "Create an assortment review from these catalog and margin notes"

Workflow

  1. Capture the review objective, such as cleanup, gap discovery, expansion planning, or seasonal review.
  2. Normalize the likely assortment signals: revenue, margin, returns, inventory, and variant coverage.
  3. Apply a portfolio lens across hero, core, seasonal, long-tail, and duplicate-risk products.
  4. Highlight likely gap areas, overlap clusters, and execution priorities.
  5. Return a markdown brief with keep-add-expand-merge-retire guidance and a 30-day plan.

Inputs

The user can provide any mix of:

  • catalog exports or summarized SKU lists
  • category, subcategory, price, margin, and launch-age notes
  • performance signals such as revenue, units, conversion, returns, ratings, or sell-through
  • variant structure such as size, color, pack size, or material
  • business goals such as premiumization, bundle strategy, entry-price coverage, or seasonal cleanup
  • operating constraints such as shelf space, warehouse capacity, cash limits, or protected hero products

Outputs

Return a markdown assortment brief with:

  • assortment health summary
  • scorecard lenses and evidence gaps
  • coverage and gap map
  • duplicate-risk or cannibalization watchlist
  • keep-add-expand-merge-retire recommendations
  • 30-day execution brief with likely owners
  • assumptions, confidence notes, and limits

Safety

  • Do not claim access to live catalog or marketplace data.
  • Treat cannibalization as an informed hypothesis, not proven causality.
  • Do not auto-retire, merge, or reprice products.
  • Downgrade recommendations when taxonomy, margin, or demand evidence is incomplete.
  • Keep strategic SKU decisions human-approved.

Best-fit Scenarios

  • DTC or marketplace catalogs with roughly 30 to 2,000 active SKUs
  • regular category reviews, quarterly assortment planning, or pre-promo cleanup
  • teams that want a lighter decision layer than a full merchandise-planning suite
  • consultants who need a fast first-pass assortment memo

Not Ideal For

  • store-level planogram planning for large physical retail networks
  • businesses with no structured catalog or product taxonomy at all
  • workflows that need automatic listing edits, delisting, or system sync
  • highly regulated approvals where assortment change requires formal governance

Example Output Pattern

A strong response should:

  • show the likely assortment shape, not just list products
  • separate hero, core, seasonal, long-tail, and duplicate-risk logic
  • explain where the catalog is overbuilt or under-covered
  • recommend next actions with impact, confidence, and owner hints
  • include a short assumptions block when the evidence is partial

Acceptance Criteria

  • Return markdown text.
  • Include health, gap, recommendation, and execution sections.
  • Make the advisory framing explicit.
  • Keep the brief practical for merchandisers and ecommerce operators.

适合场景

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OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

93.57%
按下载量换算837

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

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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