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Catalog SKU Matcher India

提供印度电商目录跨店商品匹配的分层策略(硬标识、变体规范化、软相似、置信度评分),给出误匹配防护规则与输出格式(规范 SKU 候选、带置信度的匹配列表、拒绝原因码、人工复核队列),并附规则、评分、示例与校验清单文档,不含可执行匹配代码。

anugotta
anugotta
热度 45.50
最近更新 2026-05-11 17:33:53
分类:效率依赖:纯提示词应用场景:数据处理 / 市场分析

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add catalog-sku-matcher-india
Metadata
name
catalog-sku-matcher-india
description
Match and normalize product listings across Indian ecommerce catalogs with variant-aware rules, confidence scoring, false-match prevention, and review queues for ambiguous pairs.
metadata
{"openclaw":{"emoji":"🧩","homepage":"https://clawhub.ai/Michael-laffin/price-tracker"}}

Catalog SKU Matcher India

Purpose

Build reliable cross-store product matching for Indian catalogs so price comparison is accurate.

Disclaimer

This skill provides matching and normalization guidance only. It does not guarantee perfect match accuracy for all catalogs or seller data quality.

Use at your own risk. The skill author/publisher/developer is not liable for direct or indirect loss, incorrect match decisions, trading losses, or other damages arising from use or misuse of this guidance.

Matching strategy

Use a layered approach:

  1. Hard identifiers

    • model number / GTIN / MPN / ISBN where available
  2. Variant normalization

    • brand
    • model family
    • storage/RAM
    • color
    • size/pack quantity
    • condition (new/refurbished/used)
  3. Soft similarity

    • token similarity on cleaned title
    • key-attribute overlap
    • seller metadata sanity checks
  4. Confidence score

    • high: auto-match
    • medium: human review queue
    • low: reject

False-match guardrails

  • Never match different storage/RAM variants as same SKU.
  • Never match bundles/accessories to standalone products.
  • Never ignore refurbished/used condition differences.
  • Require manual review when two or more variant fields are missing.

Output format

When matching listings, return:

  1. canonical SKU candidate
  2. matched listings with confidence level
  3. rejected candidates with reason codes
  4. manual review queue entries

Setup

Read setup.md and define normalization dictionaries first.

Validation

Run validation-checklist.md on labeled test sets before production.

References