AI Prompt to Audit Shopify Inventory and Listings via MCP

ROLE
You are a data analyst helping design a customer segmentation scheme.

CONTEXT
Business type: [BUSINESS_TYPE, e.g. subscription SaaS, e-commerce retailer]
Available fields: [LIST_OF_FIELDS, e.g. signup_date, last_order_date, total_orders, total_spend, plan_tier]
Approximate data ranges: [FIELD_RANGES, e.g. total_orders typically 1-40, last_order_date spans the past 2 years]
Business goal for this segmentation: [GOAL, e.g. identify customers to target for a win-back campaign]

TASK
Propose 4-6 customer segments that serve the stated goal. For each segment, provide:
1. A clear, specific name.
2. The exact rule or threshold on the available fields that defines membership.
3. A one-sentence description of what distinguishes this group.
4. One recommended action specific to this segment.

CONSTRAINTS
- Only use the fields listed above; do not assume data that wasn't mentioned.
- Make segment rules mutually exclusive where possible, and note any customers who might not fit cleanly into any segment.
- Keep each segment's rule specific enough to implement as a filter or query.

OUTPUT FORMAT
A numbered list of segments, each with Name, Rule, Description, and Recommended Action as labeled sub-points, followed by one line noting any edge cases not covered.

Extract the following fields from the text below: [FIELD 1, FIELD 2, FIELD 3, FIELD 4].

Output rules:
- Return strictly a JSON array of objects, one per entity found.
- Use exactly these keys: [key_1, key_2, key_3, key_4].
- If a value is missing or unclear, use null. Never guess.
- Do not wrap the output in markdown code fences.
- Do not add any explanation before or after the JSON.

Text:
"""
[PASTE TEXT]
"""

ROLE: You are a data cleaning assistant helping standardize a spreadsheet dataset for analysis.

CONTEXT: Below is a sample of the dataset. Columns are: [LIST OF COLUMN NAMES]

[PASTE SAMPLE DATA HERE, INCLUDING KNOWN PROBLEM ROWS]

TASK:
1. Review each column and identify formatting inconsistencies (e.g. date formats, capitalization, whitespace, abbreviations, units)
2. Propose and apply a single standard format for each column: [SPECIFY TARGET FORMATS WHERE KNOWN, e.g. dates as YYYY-MM-DD]
3. Flag rows that look like duplicates or outliers, but do not delete them — mark them for my review instead
4. Do not alter these columns without flagging first: [COLUMNS REQUIRING REVIEW BEFORE CHANGES, e.g. customer name, ID numbers]

CONSTRAINTS:
- Preserve every original row unless I confirm a deletion
- Do not invent or infer missing values — leave them blank and flag them
- [ANY ADDITIONAL CONSTRAINT, e.g. keep a specific column's original casing]

OUTPUT FORMAT:
1. The cleaned dataset as a table
2. A change log listing each column, what inconsistency was found, and what standard was applied
3. A separate list of flagged rows (possible duplicates/outliers) with a one-line reason for each flag