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Skill

/support-mining

Use when turning store reviews, support threads, crash clusters or feedback into ranked product findings — by data-analyst post-launch, and by product-researcher when existing user evidence is the question. A review-mining pass, not a standing role.

From plugin
app-dev-team
432 skills30 agents27 commands2 hooks
Install
$ npx -y skills add vmobifystudio/app-dev-team --skill support-mining --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/support-mining

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when turning store reviews, support threads, crash clusters or feedback into ranked product findings — by data-analyst post-launch, and by product-researcher when existing user evidence is the question. A review-mining pass, not a standing role.

SKILL.md

support-mining.SKILL.md
name: support-mining
description: Use when turning store reviews, support threads, crash clusters or feedback into ranked product findings — by data-analyst post-launch, and by product-researcher when existing user evidence is the question. A review-mining pass, not a standing role.

Support mining

Users describe symptoms, in their own words, having already worked around the problem. This is the pass that turns that into something the board can act on — and the discipline that keeps it from becoming a list of the loudest complaints.

Sources, and what each is good for

| Source | Good evidence for | Systematically biased toward | |---|---|---| | Store reviews | first-run and pricing friction | the delighted and the furious, never the middle | | Support threads | reproducible defects | users willing to write in — a small, patient minority | | Crash clusters | the truth about stability | devices and OS versions you have most of | | Uninstall / churn signals | where value failed to land | nothing at all about why |

**Name the bias in the report.** A finding from reviews alone is a finding about reviewers.

The pass

1. **Cluster by user-described symptom, not by your guess at the cause.** "Photos disappeared" and "lost my edits" may be one defect or three; keep them separate until evidence merges them. 2. **Rank by `frequency × severity × recency`**, and show all three columns. A cluster ranked by volume alone buries the data-loss report mentioned twice. 3. **Attach evidence to each cluster** — verbatim quotes with dates, app version, device and OS where available. A cluster with no verbatim is your paraphrase, and you label it as such. 4. **Separate a defect from a missing feature from a misunderstanding.** All three arrive as "it doesn't work" and they route to three different people. A misunderstanding is a `content-design` finding, not a bug. 5. **Check each cluster against `knowledge/failure-corpus.md`.** A cluster matching a known class that already has a shipped rule is a **recurrence** — the rule did not work, and that is a strictly more valuable finding than the incident.

The rules

  • **Never fabricate or paraphrase a quote into something cleaner.** Verbatim or nothing.
  • **A cluster is not a ticket.** Hand `qa-engineer` a reproduction attempt; a bug filed from a review

without one wastes a developer's day.

  • **Report what you could not tell.** Reviews rarely say version or device; guessing which release

regressed is how a good release gets rolled back.

Output

A dated `## Support findings` section in `docs/51-bugs.md`: ranked clusters with their three scores, verbatim evidence, the classification (defect · missing feature · misunderstanding · recurrence of FC-NNN), and the proposed owner for each.

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