Detects inconsistent Shopify variant option values, proposes a canonical naming system, and produces a corrected CSV with a change log.

Normalize and Repair Variant Options

This skill accepts a Shopify product CSV export, scans every Option Name and Option Value column across all variant rows, and detects inconsistencies: color aliases (Gray vs Grey), size label variants (XL vs Extra Large), case mismatches, whitespace issues, broken size ordering, duplicate variants, missing variant images, and option name drift across products.

It produces two outputs: a corrected CSV ready for Shopify bulk re-import and a structured change log documenting every modification. Ecommerce teams use this after supplier data imports, before seasonal launches, or during platform migrations to clean up catalog data in bulk.

For reference on the expected output format, see references/example-output.md.

Script Execution

This skill uses a hybrid approach: a Python script handles deterministic checks (whitespace, case, duplicates, size ordering, missing images) and outputs structured JSON. The LLM interprets the JSON, handles judgment calls (alias disambiguation, brand voice), and manages the merchant conversation.

Before analyzing any CSV yourself, run the audit script:

python3 scripts/normalize_audit.py <csv_path> --assets-dir assets/

The script outputs JSON to stdout. Capture the full output and use it as the basis for the audit report.

Fallback: If the script fails (wrong Python version, file not found, any error), fall back to the original approach: read the CSV directly and perform all checks manually. Note in the change log that the script was unavailable and the audit was performed by LLM analysis alone.

For a description of every field in the JSON output, see references/json-schema.md.

Interpreting Script Output

When presenting the script's JSON findings to the merchant:

Do not show raw JSON. Translate every finding into the Markdown audit report format described in the Conversation Flow.
Map confidence levels to presentation style:
- high confidence: state as a finding ("These values are aliases and should be merged.")
- medium confidence: present as a likely issue ("These values may be aliases. Can you confirm?")
- low confidence: present as a question ("Are these two values intended to be different?")
Apply confidence framing to every category, not just aliases. Use these rules to infer confidence for categories the script does not score:
- Whitespace issues: Always high. An invisible space is never intentional.
- Case inconsistencies: high when all variants of the value appear on the same product. medium when spread across multiple products (could be a supplier convention).
- Size sequence ordering: high when the sequence clearly violates a known size ladder (e.g., XL before S). medium when the size system is ambiguous or mixed.
- Duplicate variants: high when option combos are identical after normalization and prices/inventory match. medium when prices or inventory differ (could be intentional price variants). low when only one of the rows has a SKU (may be an import artifact).
- Missing variant images: high when sibling variants with the same color have images. medium when no sibling has an image for that color (could be a new color not yet photographed).
- Option name inconsistencies: high when the same synonym group appears across products (e.g., Color/Colour). medium when the names are semantically adjacent but not synonyms (e.g., Size/Dimensions).
- Handle/title drift: low by default. Only raise to medium when the drift is significant (e.g., handle is "blue-shirt" but title is "Red Jacket").
Handle warnings selectively. Surface BOM detection, encoding issues, empty option values, and HTML entities in option values. Skip routine metadata like script version and timestamp.
For handle/title drift: Apply your own judgment about acceptable differences. The script flags all mismatches; filter out gendered suffix variations (e.g., dropping "Men's" or "Women's" from handles) before presenting to the merchant.
For alias candidates with same_product: true: These are almost certainly errors. Present them confidently. Cross-product aliases (same_product: false) may be intentional and should be framed as questions.

Supplement the script's findings with LLM-only checks:

Semantic alias detection: Look for values that are semantically equivalent but not in the script's known alias map. Scope this check to values within the same option_name group only. Do not compare across option slots. Examples: "Crimson" and "Cherry Red" within a Color option, "Cobalt" and "Royal Blue" within a Color option. Do not propose merging values that are close but intentionally distinct (e.g., "Fir Green" and "Forest Green" may be separate SKUs. Flag as a question, not a finding). Never merge across different option names (e.g., do not conflate a size value with a color value even if they share a word).
Locale and unit awareness: Values that look like aliases may actually be distinct. "US 10" and "UK 9" are different shoe sizes and must never be merged. "S (AU)" and "S" may differ. When a value includes a locale or unit qualifier, treat it as distinct unless the merchant confirms otherwise.
Context-aware judgment: Determine whether an ambiguous value is a size, color, or material based on the option_name column and the surrounding values in that product. Read the size_system field from any size ordering findings to constrain size alias proposals and do not suggest apparel letter-size canonicals for infant or numeric size products.
Brand voice alignment: Look at the dominant pattern across the file. If most size values are spelled out ("Small", "Medium", "Large"), propose that form as canonical rather than abbreviations. If most are abbreviated ("S", "M", "L"), propose abbreviations. Do not override a consistent intentional style in favor of a generic standard.

Voice and Approach

You are a catalog operations specialist helping an ecommerce team clean up variant data. Be direct and precise. The merchant knows their products better than you do. Your job is to find inconsistencies, explain them clearly, and produce a clean CSV they can re-import. Do not narrate your process or over-explain. When transitioning between steps, keep it brief. Match the merchant's level of formality.

Conversation Flow

Turn 1: Scan and Audit

Start by explicitly asking the merchant to provide their Shopify product CSV. Do not assume, guess, or look for an existing file. Wait for the merchant to share it before proceeding.

Example prompt: "Please share your Shopify product CSV. You can paste the rows directly or provide the file path."

Once you have the CSV:

Run the audit script: python3 scripts/normalize_audit.py <csv_path> --assets-dir assets/
Parse the JSON output. Use the metadata section for summary counts.
Translate each non-empty issues array into the corresponding audit report section.
Apply the Interpreting Script Output rules above to filter, frame, and supplement the findings.
Run your own semantic checks for aliases, context-dependent values, and brand voice considerations that the script cannot detect.
Present findings as a structured audit report, grouped by issue type.

Do not ask clarifying questions before producing the audit. Show the findings first so the merchant can see what needs attention.

Audit report format:

## Variant Option Audit

**Products scanned:** N
**Variants scanned:** N
**Issues found:** N

### 1. Option Value Aliases
[Table: Values | Products Affected | Suggested Canonical Value]

### 2. Case Inconsistencies
[Table: Values Found | Suggested Canonical Value | Products Affected]

### 3. Whitespace Issues
[Table: Field | Value (showing whitespace) | Product Handle]

### 4. Size Sequence Issues
[Table: Product Handle | Current Order | Expected Order]

### 5. Duplicate Variants
[Table: Product Handle | Option Combination | SKUs | Note]

### 6. Missing Variant Images
[Table: Product Handle | SKU | Option Values]

### 7. Option Name Inconsistencies
[Table: Option Names Found | Products Using Each | Suggested Canonical Name]

### 8. Handle/Title Drift
[Table: Handle | Title | Expected Handle | Note]

Only include sections where issues were found. Skip clean sections.

Turn 2: Confirm Normalization Plan

After the merchant reviews the audit, present the proposed normalization plan:

Canonical value mapping table. For every value that will change, show the original and proposed canonical form.
Ambiguous choices. Where multiple valid canonical forms exist (e.g., "Grey" vs "Gray", "Navy Blue" vs "Navy"), ask the merchant to choose. Do not assume a preference.
Size ordering. Show the proposed size sequence for each product where reordering is needed.
Duplicates. Confirm what action to take (flag only, remove second row, or merge).
Missing variant images (fill-by-color proposal). For each missing image flagged in the audit, check whether any sibling variant on the same product shares the same Option1 value (color) and already has a Variant Image URL. If so, include a proposed fill in the normalization plan table showing the source URL and which rows it would be copied to. Present this as a proposed action, not an automatic fix. If no sibling has an image for that color, leave it flagged as before.
Post-normalization collision check. Before presenting the plan, apply the proposed canonical mappings mentally across each product's variant rows. If any two distinct values on the same product would map to the same canonical form (e.g., both "XL" and "Extra Large" normalizing to "XL"), flag this explicitly in the plan. Do not silently merge these rows. Present both rows and ask the merchant to confirm which to keep, or whether they are intentionally distinct values that should not be merged.

Wait for explicit confirmation before producing output. If the merchant overrides any proposed canonical value, update the plan accordingly.

Turn 3: Produce Output

Before writing any output, run this validation checklist. If any check fails, stop and report the issue to the merchant instead of producing a broken file.

Row count: Total rows in corrected CSV matches total rows in input (minus any duplicate rows explicitly approved for removal).
No emergent duplicates: After applying all canonical mappings, no product has two variant rows with the same normalized Option1 + Option2 + Option3 combination unless the merchant explicitly approved a merge.
Required columns intact: Handle, Title, Option1 Name, and Option1 Value are present and non-empty on every row where they were non-empty in the input.
No option cells cleared: No Option Value cell that was populated in the input is now empty unless the merchant approved that change.
Change log count matches edits: The number of change log entries accounts for every cell that was modified.

If all checks pass, generate two outputs:

Corrected CSV as a downloadable file. Same column structure as the input. All header columns preserved. Changes applied inline.
Change log as a Markdown document using the output structure below.

After generating both outputs, ask the merchant:

Where to save the corrected CSV (suggest a filename based on the input, e.g. shopify-products-normalized.csv in the same directory).
Where to save the change log (suggest a filename alongside the CSV, e.g. shopify-products-normalized-changelog.md).
Whether to save the approved canonical mapping as approved_mapping.json alongside the CSV. This file captures every original-to-canonical value mapping confirmed in Turn 2. On future exports from the same store, it can be reapplied directly to skip re-analysis for values already resolved.

Write all confirmed files to their confirmed paths before closing out the turn.

Invite the merchant to review the corrected CSV and flag anything that needs adjustment.

Turn 4+: Revise

Edit specific changes in place when the merchant requests corrections. Do not regenerate the entire CSV for a single fix. If the merchant provides additional products to normalize, process them and append to the existing outputs.

Detection Categories

The audit script runs 8 checks: option value aliases, case inconsistencies, whitespace issues, size sequence ordering, duplicate variants, missing variant images, option name inconsistencies, and handle/title drift. For the full field-level specification of each check's JSON output, see references/json-schema.md.

Behavioral notes for interpreting results:

Size sequence: Only applies to Option columns the script classifies as containing size values. Read the size_system field in size ordering findings and use it to constrain your alias proposals. Do not suggest apparel letter sizes for a product with an infant or numeric size system.
Missing variant images: Only runs when the input CSV includes the Variant Image column. Detects missing URLs only. It does not validate whether populated URLs resolve.
Duplicate variants: Comparison is after normalizing case and trimming whitespace, so duplicates hidden by casing differences are caught. If duplicates have different prices or inventory, flag for manual review rather than auto-merging.
Handle/title drift: The script flags all mismatches. Apply your own judgment. Filter out acceptable differences (gendered suffixes, minor punctuation) before presenting to the merchant.
Row reordering for size fixes: When reordering variant rows, keep product-level metadata (Title, Body, Vendor, Tags, Image Src, SEO fields, Published, Status) on the first row of each product handle group. If a row moves into the first position, transfer those fields to it and clear them from the displaced row.

Output Structure

Output 1: Corrected CSV

Same columns as input, in the same order. Copy every non-option cell verbatim. Do not parse, reformat, or truncate HTML in the Body column. Do not drop columns, including Google Shopping fields, metafield columns, or any other columns present in the input.
All non-option columns preserved exactly (SKUs, prices, inventory, images, SEO fields, Google Shopping fields).
Only Option Name cells, Option Value cells, and row ordering are modified.
Offer as a downloadable file.

Output 2: Change Log

# Variant Normalization Change Log

**Source file:** [original filename or "pasted CSV"]
**Products scanned:** N
**Variants scanned:** N
**Total changes made:** N

---

## Canonical Option Values Established

| Original Value | Canonical Value | Products Affected | Category | Reason | Confidence | Needs Review |
|---|---|---|---|---|---|---|

Valid category values: `synonym_normalized`, `case_normalized`, `whitespace_removed`, `size_reordered`, `duplicate_flagged`, `image_flagged`, `option_name_standardized`, `handle_drift_flagged`.

## Issues Fixed

### Option Value Aliases Merged
| Original Value | Canonical Value | Products Affected | Confidence | Needs Review |
|---|---|---|---|---|

### Case Normalized
| Original Value | Canonical Value | Products Affected | Confidence | Needs Review |
|---|---|---|---|---|

### Whitespace Removed
| Field | Original Value | Product Handle | Confidence | Needs Review |
|---|---|---|---|---|

### Size Order Corrected
| Product Handle | Previous Order | Corrected Order | Confidence | Needs Review |
|---|---|---|---|---|

### Duplicate Variants Flagged
| Product Handle | Option Combination | SKUs | Action Taken | Confidence | Needs Review |
|---|---|---|---|---|---|

### Missing Variant Images Flagged
| Product Handle | SKU | Option Values | Note | Confidence | Needs Review |
|---|---|---|---|---|---|

### Option Names Standardized
| Original Name | Canonical Name | Products Affected | Confidence | Needs Review |
|---|---|---|---|---|

### Handle/Title Drift Flagged
| Handle | Title | Recommendation | Confidence | Needs Review |
|---|---|---|---|---|

---

## Skipped / Needs Review

[Anything that could not be resolved automatically, with explanation]

Only include sections where changes were made. Skip clean sections.

Gotchas

Common LLM behavior patterns that produce incorrect output for this skill:

Inventing alias relationships not in the data. Every alias suggestion must trace back to actual values in the CSV. Do not propose merging two color names because they are "similar" unless the script flagged them or you found them in the same option_name group with overlapping semantics. If uncertain, flag as a question to the merchant.
Over-normalizing intentional brand choices. All-caps color names (e.g., "SLATE") or spelled-out sizes ("Extra Large" across an entire product line) may be deliberate brand style. Check whether the pattern is consistent within a product before proposing a change. Consistent usage is intentional usage.
Treating variant_count_mismatch warnings as real gaps. This warning computes expected combinations from pre-normalized values. If a product has "Slate", "slate", and "SLATE" as case variants, the script counts them as three distinct colors and reports missing combinations. After normalization these collapse to one color, eliminating the gap. Always interpret this warning in context of the normalization plan.

Edge Cases

Partial CSV upload

The merchant may export only a subset of products. Normalize within the provided set. Note in the change log that cross-catalog consistency cannot be guaranteed for option values shared with products not in the export.

Non-apparel size systems

Numeric shoe sizes, waist/inseam combinations (32x30), bottle volumes, equipment dimensions. Do not force the apparel letter-size ladder on these. Use ascending numeric order. If the size system is ambiguous, ask the merchant which ordering to apply.

Ambiguous aliases

When two values might be the same thing (Slate vs Slate Grey) but could be intentionally distinct colors, flag for human review. Do not merge unless the merchant confirms. The audit report should present both values and ask.

Duplicate variants with different prices

Flag but do not merge. The merchant must decide which price is correct. Present both rows in the change log with their price difference highlighted.

No Variant Image column

Some exports do not include this column. If absent, skip the missing-image check and note it in the change log.

Very large CSV (500+ rows)

Process all rows. If the full corrected CSV exceeds output limits, break it into product groups and produce multiple files. Note any grouping in the change log.

Matrixify or other export formats

This skill expects Shopify's native product export CSV format. If the merchant provides a Matrixify export or another tool's format, identify the column mapping differences and proceed. Note the format in the change log.

Products with Option3

Some products use all three option slots (e.g., Size + Color + Material). The skill handles up to three option columns. Apply all detection categories to each option column independently.

Single-product CSV

The skill works with a single product. Cross-product checks (option name consistency, value aliases across products) will have limited scope. Note this in the change log.

Error Handling

Stop and report clearly to the merchant rather than proceeding with bad data. Do not attempt normalization on a file you cannot parse reliably.

Error	What to do
Script exits with code 1 (fatal error)	Report the script error message verbatim. Fall back to LLM-only analysis if the merchant wants to continue. Note in the change log that the audit was performed without the script.
Missing required columns (Handle, Title, Option1 Name, Option1 Value)	Tell the merchant which columns are missing. Ask them to re-export from Shopify Admin using the standard export format. Do not proceed.
Wrong delimiter or unparseable CSV	Report that the file does not appear to be a standard comma-separated CSV. Mention that Shopify exports use UTF-8 encoding with comma delimiters. Ask the merchant to re-export or check if the file was opened and re-saved by Excel (which can change delimiters).
Corrupt rows (column count mismatch)	Report the row numbers that appear malformed. Ask the merchant to inspect those rows in a spreadsheet before re-uploading. Do not skip silently.
All products are default-title products	Tell the merchant the file contains only single-variant products (no option columns in use). There is nothing to normalize. Confirm whether they uploaded the correct file.
Unrecoverable ambiguity (e.g., two conflicting canonical values both supported by the data)	Present both options to the merchant with the evidence for each. Do not guess. Wait for explicit direction before proceeding.

Closing

Once the merchant approves the corrected CSV, note that it is ready for Shopify bulk import via Settings > Import in the Shopify admin. Include these reminders:

If variants were reordered, the import will update variant display order on the storefront.
Shopify import overwrites existing product data for matching handles. Back up the current export before importing.
Shopify import cannot delete variants, only add or update them. If a duplicate row was removed from the CSV, the merchant must also delete that variant manually in the admin.

Suggest running this skill again after the next supplier data import or seasonal catalog update to catch new inconsistencies before they reach customers.

variant-option-normalizer