ecommerce returns support builder

    1

    Converts store return policies into structured support tables, decision trees, and customer response templates.

    $5

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    ecommerce returns support builder

    Example session with this skill installed

    Analyze this return policy for a US electronics store: 30 days for most items, 15 days for laptops, must be in original packaging. 15% restocking fee for opened boxes. Buyer pays shipping.

    • Read your context and instructions
    • Compiled the ecommerce returns support

    I have normalized the electronics store return policy into an operational support pack, including a decision tree and response templates.

    Operational Support Pack: Electronics Store

    Policy Facts Table

    ComponentValue (from policy)Notes
    EligibilityAll buyersStandard retail policy
    Window30 days (General) / 15 days (Laptops)Window starts from delivery
    ConditionsOriginal packaging requiredOpened items incur a fee
    ExceptionsNone listedPolicy is silent on final sale
    Shipping ResponsibilityBuyer paysRegardless of reason (Policy Fact)
    RefundsForm not specifiedAssumed original payment [AMBIGUOUS]
    ExchangesNot mentionedPolicy is silent
    EvidenceNot specifiedSuggest requesting photos for opened boxes
    ApprovalsStandardNo RMA mentioned
    EscalationNot specifiedStandard support flow
    OwnershipNot specifiedLikely self-service or manual label
    Customer MessagingProfessionalNo specific phrases provided

    Decision Path

    1. Is the item a laptop?
      • Yes: Is today <= Delivery + 15 days? (If no, Deny)
      • No: Is today <= Delivery + 30 days? (If no, Deny)
    2. Is the item in original packaging?
      • Yes: Proceed to full refund (minus shipping).
      • No: Opened Box Path -> Notify customer of 15% restocking fee.
    3. Reason for return?
      • Defective: Check local consumer law [LEGAL] for shipping fee override.
      • Change of Mind: Buyer pays shipping per policy.

    Response Guidance

    Outcome: Opened Box Return (Laptop)

    Message: "We can process a return for your laptop; however, as the packaging has been opened, a 15% restocking fee will apply per our policy. Would you like to proceed?"

    • Evidence: Photo of the opened box and serial number.
    • Next Step: Calculate 15% of MSRP, generate return authorization.

    Validation Report

    • Verified Facts: Dual-tier return window (15/30 days) and restocking fee are explicitly stated.
    • Ambiguities: Policy does not specify if "30 days" starts from order date or delivery date.
    • Assumptions: Assumed "original packaging" includes all internal components and manuals.
    • Legal Note: In some US states, restocking fees must be clearly disclosed at point of sale to be enforceable.

    Next steps

    1. Clarify if the return window starts at order or delivery.
    2. Define the process for defective items to avoid legal friction regarding shipping costs.
    3. Confirm if exchanges are permitted to reduce refund churn.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    Customer support teams often struggle with inconsistent return approvals because policies are buried in vague, unstructured text. Manually cross-referencing store rules against local consumer laws and live order data leads to costly shipping errors and poor buyer experiences.

    What it does

    • Normalizes raw return policy text into a structured 12-point operational table covering eligibility, windows, and shipping responsibility.
    • Maps decision paths for common scenarios like defective items, change of mind, and final sale exclusions.
    • Generates precise customer-facing response templates based on specific order outcomes.
    • Flags conflicts between store policy and jurisdiction-specific legal rights like the EU 14-day cooling-off period.
    • Identifies policy ambiguities and unsupported assumptions before they reach the customer.

    Why this beats prompting it yourself

    A standard LLM often hallucinates return windows or misses subtle exclusions hidden in legal jargon. This skill enforces a rigorous normalization process that separates policy facts from legal mandates, ensuring every support decision is grounded in a verified evidence chain rather than a best guess.

    Use cases

    • Building a support knowledge base for a new Shopify or WooCommerce storefront.
    • Auditing existing return policies for compliance with the UK Consumer Rights Act or similar regional laws.
    • Creating standardized macros and decision trees for outsourced support teams.
    • Handling complex return disputes where policy facts conflict with buyer claims.

    Known limitations

    Requires the user to provide the initial policy text or URL. It cannot browse authenticated portals or internal company wikis without direct input.

    Requires raw policy text or URL as input. Does not integrate directly with live shipping carriers or order management system APIs without manual data entry of order state.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Listed2 months ago

    What's inside

    Frequently Asked Questions

    Simply provide the skill with your store's return policy text or a direct URL. The tool will automatically extract key terms and organize them into a structured operational table and decision tree for your support team.

    The skill identifies potential conflicts between your store rules and regional mandates, such as the EU’s 14-day cooling-off period or the UK Consumer Rights Act, highlighting where your policy might be legally unenforceable.

    You receive the full logic framework, including decision trees, standardized response templates for common return scenarios, and a normalized 12-point policy table ready for your knowledge base.

    This skill is designed for any AI agent that supports structured text processing and is ideal for integration into customer service platforms like Zendesk or Gorgias via your preferred AI workspace.

    Unlike a standard prompt, this skill uses a multi-step normalization process to prevent hallucinations of dates or fees, ensuring every customer response is based on verified policy data rather than an LLM guess.

    Yes, you can re-run the builder whenever you update your store’s terms to generate refreshed templates and ensure your support team’s decision paths remain current.

    New in E-commerce