--- name: duplicate-listing-detector description: "Analyzes a set of product listings and flags likely duplicates and variant mismatches using weighted similarity across title, description, image signals, and structured attributes — producing a ranked match report with confidence scores and a recommended merge, variant-group, or keep-separate action for each cluster. Use when auditing a catalog for redundant listings, cleaning up post-migration data, deduplicating supplier feeds, or fixing variant grouping errors." argument-hint: "[listing-set] [attribute-schema]" allowed-tools: ["Read"] --- # Duplicate Listing Detector ## Purpose Compare a set of product listings and surface likely duplicates and misgrouped variants — with a confidence score, the specific signals that drove the match, and a recommended action (merge, group as variants, or keep separate) for each cluster — so catalog teams stop shipping redundant SKUs and broken variant families to the storefront. ## When to Use Invoke this skill when the user asks to: - Find duplicate product listings in a catalog or product feed - Clean up redundant SKUs after a platform migration or catalog import - Deduplicate merged supplier or dropship feeds - Detect listings that should be grouped as variants but were created separately - Catch variant mismatches (same parent, wrong child attributes) - Audit a catalog before a marketplace launch or feed sync ## Inputs Required Before starting, collect: - **Listing set**: The products to compare — pasted rows, a table, or a structured list (SKU/ID + fields) - **Fields available per listing**: Which of title, description, image reference/URL, brand, price, and attributes are present - **Attribute schema**: The structured attributes that define a variant vs. a distinct product (e.g., size, color, capacity, pack count) - **Variant policy**: What differences make items variants of one product vs. genuinely separate products - **Match sensitivity**: Aggressive (flag more, review-heavy) / Balanced / Conservative (flag only near-certain matches) - **Identifier fields**: Any GTIN, UPC, EAN, MPN, or ASIN present (hard-match anchors) - **Output scope**: Full report on all clusters, or only actionable clusters above a confidence threshold If variant policy is missing, default to: differences limited to size/color/pack are variants; differences in core function, brand, or model are separate products. If match sensitivity is missing, default to Balanced. ## Instructions ### Step 1: Normalize Every Listing Before comparing, standardize each field to remove false differences: - **Title**: Lowercase; strip punctuation and duplicate whitespace; expand common abbreviations (pcs→piece, oz→ounce, blk→black); remove marketing filler (best, premium, new, sale, 2024) - **Attributes**: Map synonyms to canonical values (grey/gray, XL/extra large, 16oz/1lb); normalize units - **Identifiers**: Trim leading zeros and formatting from GTIN/UPC/EAN/MPN - **Images**: Note filename, dimensions, or URL stem if available; treat identical image references as a strong signal - Record any field that is missing per listing — missing data lowers confidence, it does not create a match ### Step 2: Apply Hard-Match Anchors First If two listings share the same normalized GTIN/UPC/EAN or same brand+MPN, treat them as a near-certain match regardless of title differences. These override text similarity. Flag conflicting identifiers on otherwise identical listings as a **data integrity issue**, not a duplicate. ### Step 3: Score Similarity Across Weighted Signals For each candidate pair (or cluster), compute a similarity contribution per signal. Use these default weights, adjustable by match sensitivity: - **Identifier match (weight 40)**: Same GTIN/UPC/MPN = full weight. Absent = 0, not negative. - **Title similarity (weight 25)**: Token overlap after normalization. Distinguish shared core tokens (product noun, brand, model) from incidental ones. - **Attribute match (weight 20)**: Share of matching canonical attributes. Note which attributes differ. - **Description similarity (weight 10)**: Phrase and spec overlap; ignore boilerplate shipping/returns text. - **Image signal (weight 5)**: Identical or near-identical image reference. Sum into a 0–100 **confidence score**. Sensitivity shifts the flag threshold: Aggressive ≥50, Balanced ≥65, Conservative ≥80. ### Step 4: Classify Each Flagged Cluster Assign every cluster above threshold to one type: - **Exact duplicate**: Same product, same variant — all defining attributes match. Recommend merge/delete redundant. - **Variant mismatch**: Same product, differ only on variant-policy attributes (size/color/pack). Recommend group under one parent. - **Near-duplicate / ambiguous**: High text similarity but a differing core attribute. Recommend manual review with the specific field to check. - **False friend**: High title overlap but different product (e.g., "iPhone case" vs "iPhone charger"). Recommend keep separate — surface so the operator understands why it was almost flagged. ### Step 5: Produce the Match Report For each cluster, output: the listing IDs, cluster type, confidence score, the specific signals that matched, the specific fields that differ, and one recommended action. Rank clusters by confidence descending. End with a summary count by cluster type. ## Output Format - **Structure**: A ranked cluster report (table or structured block per cluster) followed by a summary tally - **Length**: Scales with input; 150–300 words of setup plus one entry per cluster - **Format**: Table for the cluster list (Cluster ID | Listing IDs | Type | Confidence | Key signals | Differing fields | Action); structured bullets for any data integrity flags - **Sections**: Normalization notes → Hard-match / integrity flags → Ranked duplicate & variant clusters → Summary tally by type - **Tone**: Catalog operations specialist — precise, evidence-first, no hedging on the recommended action ## Quality Checklist Before delivering output, verify: - [ ] Every flagged cluster has a numeric confidence score and a stated flag threshold - [ ] Each cluster names the specific signals that matched AND the specific fields that differ - [ ] Every cluster has exactly one recommended action (merge / group as variants / keep separate / review) - [ ] Identifier conflicts are reported as data integrity issues, not treated as duplicates - [ ] Variant mismatches are distinguished from exact duplicates per the stated variant policy - [ ] The summary tally counts match the number of clusters listed above it ## Notes - Missing data lowers confidence but must never manufacture a match — an empty field is not a shared value. - "False friend" clusters are worth surfacing even though the action is keep-separate; they explain near-misses and build operator trust in the scoring. - For very large catalogs, run in batches by category or brand — cross-brand duplicates are rare and comparing them wastes signal budget. - When identifiers are present and clean, weight them heavily; text similarity alone is prone to grouping accessories with their base products. --- ## How to use this skill This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top. **Installing in Claude Code:** - `~/.claude/skills//SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects) - `/.claude/skills//SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills) Restart Claude Code or open a new session to register. **Using in Claude.ai, ChatGPT, Gemini, or other AI tools:** 1. 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