catalog-sku-matcher-india
Match and normalize product listings across Indian ecommerce catalogs with variant-aware rules, confidence scoring, false-match prevention, and review queues for ambiguous pairs.
Best use case
catalog-sku-matcher-india is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Match and normalize product listings across Indian ecommerce catalogs with variant-aware rules, confidence scoring, false-match prevention, and review queues for ambiguous pairs.
Teams using catalog-sku-matcher-india should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/catalog-sku-matcher-india/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How catalog-sku-matcher-india Compares
| Feature / Agent | catalog-sku-matcher-india | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Match and normalize product listings across Indian ecommerce catalogs with variant-aware rules, confidence scoring, false-match prevention, and review queues for ambiguous pairs.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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](setup.md) and define normalization dictionaries first. ## Validation Run [validation-checklist.md](validation-checklist.md) on labeled test sets before production. ## References - Rules and reason codes: [matching-rules.md](matching-rules.md) - Confidence scoring: [scoring-guide.md](scoring-guide.md) - Edge-case examples: [examples.md](examples.md)
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