Works with the AI Agents you already use

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    Customer Support Ticket Resolver

    1

    Automated support ticket triage, policy-grounded resolution drafting, and escalation logic.

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    Customer Support Ticket Resolver

    Customer Support Ticket Resolver

    Example session with this skill installed

    Ticket: My order #4402 arrived with a cracked screen. I want a refund now.
    Policy: Damaged items must be reported within 48 hours for refund. We require a photo of damage. Order #4402 delivered 3 hours ago.

    • Read your context and instructions
    • Compiled the customer support ticket

    The ticket has been analyzed against the provided damage policy and order delivery timestamp. Evidence confirms the report is within the 48-hour window, though photographic evidence is still required.

    Ticket Resolution Summary

    • Ticket: #4402 - Cracked screen refund request
    • Issue Category: Product Issue / Damage
    • Customer Intent: Refund / Problem Resolution
    • Recommended Resolution: Request Information (Photo)
    • Escalation: No Escalation
    • Confidence: High

    Evidence & Policy Check

    Requirement/FactEvidencePolicy/SourceResult
    Report WindowDelivered 3h ago / Reported nowMust be within 48hPass
    Damage ProofNot provided in ticketPhoto required for refundFail
    Order ValidityOrder #4402 providedInternal Order RecordPass

    Recommended Resolution

    • Action: Request photo of the damaged screen.
    • Reason: Policy requires visual proof before a refund can be authorized.
    • Required information: High-resolution photo of the cracked screen and the shipping box.
    • Policy basis: Damaged items policy (48h reporting / photo requirement).
    • Confidence: 95%

    Customer Response

    Hi there, I am sorry to hear that your order arrived with a cracked screen. Since the order was delivered just a few hours ago, we can certainly look into a refund for you. To proceed with the claim, please reply to this message with a clear photo of the damage to the screen and the condition of the exterior packaging. Once we receive those details, we will finalize the resolution for you.

    Internal Note

    Issue: Damaged product reported by customer.
    Intent: Full refund.
    Evidence: Order #4402 confirmed delivered today. Timeline is valid.
    Policy: Refund requires photo proof within 48 hours.
    Action: Pending customer photo.
    Escalation: None needed at this stage.
    Missing Information: Photo of damage.
    Next Step: Await customer reply with attachments.

    Next steps

    1. Send the drafted response to the customer via the helpdesk.
    2. Tag the ticket as "Awaiting Customer Evidence."
    3. Once the photo is received, process the refund per the Damaged Items Policy.

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

    About this skill

    The problem

    Support teams often struggle with inconsistent ticket resolutions and policy drift across large backlogs. Manual triage is slow, prone to human error, and frequently misses critical internal context or policy exceptions.

    What it does

    • Classifies tickets by primary and secondary issue categories and detects specific customer intent.
    • Cross-references customer claims against your specific product documentation and internal policies.
    • Generates structured evidence tables comparing ticket facts to policy requirements.
    • Drafts professional customer-facing replies alongside private internal resolution notes.
    • Determines escalation necessity based on fraud risks, safety concerns, or authorization limits.

    Why this beats prompting it yourself

    General prompts often hallucinate refund amounts or fabricate shipping promises. This skill enforces a strict separation between verified facts and customer assumptions, ensuring every response is grounded in your provided documentation rather than LLM guesswork.

    Use cases

    • Automating the first-pass triage and drafting for e-commerce return requests.
    • Standardizing technical support troubleshooting steps based on internal wikis.
    • Audit-ready ticket processing where internal notes must map directly to policy clauses.
    • Prioritizing support backlogs based on business impact and urgency signals.

    Known limitations

    Does not execute actions in CRMs directly. Requires manual input of relevant policy text and order data to avoid hallucinations. Marks missing data as Not Provided rather than searching external webs.

    Does not perform API writes to CRMs. Requires manual provision of policy/order context. Will not fabricate data if specific policy text is missing from the prompt context.

    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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    Listed1 month ago

    What's inside

    Frequently Asked Questions

    This skill automates the classification of incoming tickets, analyzes them against your specific internal policies, and drafts both customer-facing responses and internal resolution notes based on verified facts.

    The skill is designed to work with most leading LLM agents; however, it requires the user to provide the relevant policy text and order data as context to ensure responses are grounded and accurate.

    No, the Resolver acts as an intelligence layer that drafts responses and logic; it does not have direct write-access to your CRM or ticketing platform to prevent unauthorized actions.

    Purchasing this skill grants you the optimized logic flow, structured data templates for evidence mapping, and the intent classification framework tailored for support environments.

    This skill prevents hallucinations by strictly separating verified policy facts from customer claims and marking any missing information as 'Not Provided' rather than making assumptions.

    New in Business & Operations