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Support Ticket Miner

Scans support tickets, clusters repeat issues, and suggests help-doc updates and marketing angles.

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What it does

Support Ticket Miner turns a pile of support tickets into a clear map of what your customers keep hitting. It reads the batch you hand it, reduces each ticket to its core problem, and clusters the repeats so you can see which issues matter by volume instead of by who shouted loudest. Each cluster gets a root-cause read, product, docs, or messaging, plus a one-line fix direction. Then it goes two useful places at once: it tells you which help articles to rewrite or create using the customers’ own words, and it drafts marketing angles from the sharpest pains, because the problems people complain about are often the ones a landing page should answer. It never touches a ticket, never edits a doc, never sends anything. You approve every change; it does the reading.

What it includes

  • Ticket batch reader for CSV exports or pasted threads
  • Problem clustering by underlying cause with volume counts
  • Representative quote pulls in the customers’ own words
  • Root-cause diagnosis: product, docs, or messaging
  • Help-doc rewrite and new-article plan
  • Marketing angle drafts tied to each cluster

Example

Input: 200 exported tickets from a podcast hosting tool, last quarter.

Output:

CLUSTER TABLE
| Problem                          | Tickets | Share | Sample quotes                                              | Root cause |
|----------------------------------|---------|-------|------------------------------------------------------------|------------|
| RSS feed rejected by Apple       | 34      | 17%   | "keeps saying my feed is invalid", "apple rejected again"  | docs       |
| Upload stalls at 90%             | 21      | 10.5% | "gets stuck right at the end", "90 percent and nothing"    | product    |
| Import from old host fails       | 18      | 9%    | "old episodes didn't come over", "import just spins"       | product    |
| Download counts differ by app    | 15      | 7.5%  | "numbers don't match spotify", "apple shows fewer"         | messaging  |
| Cover art rejected               | 12      | 6%    | "artwork keeps getting rejected", "says wrong size"        | docs       |

Fixes: rewrite feed validation article with real error screenshots;
add resume for interrupted uploads; add a guided import with a
redirect checklist; explain how each app counts downloads on the
stats page; state exact cover art specs with a failing example.

HELP-DOC PLAN
Rewrite:
- RSS validation guide. Borrow: "rejected by apple", "keeps saying my feed is invalid"
- Cover art requirements. Borrow: "artwork keeps getting rejected", "says wrong size"
New:
- "Upload stalls at 90 percent" troubleshooting page. Borrow: "gets stuck right at the end", "90 percent and nothing"
- "Moving your show from another host". Borrow: "old episodes didn't come over", "import just spins"
- "Why your download numbers differ by app". Borrow: "numbers don't match spotify", "apple shows fewer"

MARKETING ANGLES (drafts for human review)
- [Draft, cluster 1] "Feeds that pass Apple validation the first time."
- [Draft, cluster 2] "No more '90 percent and nothing.'"
- [Draft, cluster 3] "Move your show and bring every old episode with it."
- [Draft, cluster 4] "Download numbers that explain why Spotify and Apple differ."
- [Draft, cluster 5] "Cover art checked for size before Apple sees it."

What this one touches

Reads only the tickets you paste or point it to. It never closes, deletes, or replies to a ticket, never edits a help doc, and never publishes marketing copy. A human approves every doc change and every outbound message.

Install

Copy this block and paste it into your Muse chat. Muse does the install and confirms when it's done.

Install prompt

Full source

This is the exact text Muse installs. Read it. That's the whole trust model.

SKILL.md
---
name: support-ticket-miner
description: Scan support tickets, cluster repeat issues, and suggest help-doc updates and marketing angles. Trigger phrases: "mine my support tickets", "what are customers complaining about", "find repeat support issues".
---
# Purpose

Turn a batch of support tickets into an evidence-backed map of what
customers keep running into: the repeat issues, the wording they use,
and the gaps in your docs. It reads the tickets you give it, groups
the problems into clusters, and hands you two things: which help
articles need rewriting, and which customer pains are worth talking
about in marketing.

# Workflow

1. Read the ticket batch: exported CSV, pasted threads, or a
   connection you provide. Ask for the product name and date range
   if they are missing.
2. Normalize each ticket to one core problem in one sentence.
3. Cluster tickets by underlying problem, not by surface wording;
   count each cluster and pull 2-3 representative quotes.
4. For each top cluster, diagnose the root cause as product,
   docs, or messaging, and propose a fix in one line.
5. Map clusters to help docs: which articles cover them, which
   are missing, which need a rewrite based on real customer
   language.
6. Draft marketing angles from the sharpest pains, using the
   customers' own words, labeled as drafts only.

# Output Contract

- Cluster table: problem, ticket count, share of batch, sample
  quotes, root cause category.
- Help-doc plan: rewrite list, new-article list, exact customer
  phrases to borrow.
- Marketing angles: 5-10 one-line angles, each tied to a cluster,
  marked as drafts for human review.

# Operating Rules

- Never close, delete, or reply to a ticket.
- Never publish a help-doc change or marketing copy without human
  approval.
- Never invent tickets or quotes; every claim traces to a ticket
  in the batch.
- Quote real customer language; do not smooth it into corporate
  phrasing.
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