Clone Support

Highlights

• Summarize a long support ticket for handoff

  For support agents passing a long ticket to a teammate or the next tier: get a one-page handoff with the issue, what was tried, the customer's mood, and the next step. A local script reads the thread and measures messages, time open, reply gaps, who it is waiting on, handoffs, and refund, chargeback, or safety mentions, so nothing gets lost.

  Sample run: measures the shipped thermostat thread and finds it waiting on the agent: Thread

• Write FAQ answers from your most common tickets

  For support teams and small businesses: find the questions customers ask most often in your tickets and turn them into clear FAQ entries. A local script groups tickets that share keywords, counts each group, and shows example questions and ticket ids, so the FAQ covers what people really ask. Facts you have not confirmed are marked, not guessed.

  Sample run: groups the shipped yoga studio tickets into six common questions: Faq clusters

• Turn your best support replies into macros

  For support teams who type the same answers every day: turn past replies into a library of canned responses with placeholders for names, order numbers, dates, and amounts. A local script finds the sentences and blocks agents keep repeating, swaps personal details for placeholders, and counts how often each one is used.

  Sample run: finds the exchange, tracking, and refund blocks in the shipped shoe store replies: Macros

• Draft support replies from your help docs

  For support agents: draft a reply to a customer ticket that uses only what your help docs say, with each answer cited to its doc section. A local script ranks the doc passages that best match the ticket and lists the words the docs never mention, so gaps get flagged instead of made up. Drafts only; you send.

  Sample run: ranks the cancellation, refund, and damaged item sections for the shipped ticket

• Tag support tickets by topic and urgency

  For support teams with a pile of tickets: tag each one by topic and urgency so the right ones get answered first and you see what customers write in about. A local script applies a plain keyword rule file you can edit, writes a tagged CSV, counts by topic, and lists untagged tickets with words to turn into new rules.

  Sample run: tags the shipped plant shop tickets and lists the three untagged: Tagged summary