> ## Documentation Index
> Fetch the complete documentation index at: https://helpdocs.getthread.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Sentiment Closure Report

> Closed tickets in a date range bucketed by sentiment score, per client / tech / period, with the driver messages cited so a low score is explainable — not just a number. Runs manually on demand.

<Info>
  **Category:** Reporting & Analytics · [View source ↗](https://github.com/bryan-getthread/skills/blob/main/skills/reporting-and-analytics/sentiment-closure-report/SKILL.md)
</Info>

**Connectors:** none — works with Thread out of the box

**Role:** [Service & Ops Manager](/start-here/roles/service-ops-manager)

**Outcome:** Retention & Growth (CSAT/Expansion)

**When to use:** "Sentiment on everything we closed last month," "which clients / techs had the most negative closes?", "show closure sentiment by week for \<client>," or prepping a QBR or coaching conversation with evidence, not vibes.

**Run it:** across all closed tickets in the range — manually on demand (Thread Flows are ticket-event triggered with no schedule, so this can't run itself).

## Prompt

```
How did clients actually feel about the tickets you closed? Take the closed tickets in
a range, bucket them by sentiment, break it down by client / tech / period, and —
crucially — cite the messages that drove the low (and high) scores so the report is
actionable, not just a gauge. Read-only; this changes nothing.

This runs MANUALLY on demand — Thread Flows are ticket-event triggered with no
schedule/cron, so this is not a scheduled skill; run it on request or from an external
scheduler.

1. Scope the population: closed tickets within the requested date range. Apply any
   client/tech filter asked for, looking up the client or tech names first.

2. Pull the tickets and read each ticket's sentiment score. Bucket
   them — Negative / Neutral / Positive (or the desk's score bands) — and count per
   bucket. Never invent a sentiment value or a message.

3. Break down by the axis requested: per client, per tech, or per period (week/month).
   Show counts and the share negative, not just an average that hides a bad tail — a
   handful of very negative closes matters more than the mean.

4. CITE DRIVERS. For the negative bucket (and a couple of strong positives), quote or
   paraphrase the specific message(s) that drove the score, with the ticket number — so
   a reader can see WHY. This is the point of the report.

5. RESPONSE/COVERAGE HONESTY: always show the scored-vs-total denominator — state how
   many closed tickets actually had a sentiment score. Never present sentiment on 12
   tickets as if it characterized 300 closes.

6. Attribute fairly — a negative sentiment isn't automatically the assigned tech's
   fault; present it as signal, not a verdict. Close with the one or two patterns worth
   acting on (a recurring complaint theme, a client trending down) and offer a
   drill-down.
```


## Related topics

- [Weekly Ops Report](/skill-library/reporting-and-analytics/weekly-ops-report.md)
- [Configure Magic Sentiment](/assistive-ai/configure-sentiment.md)
- [QA & Closure](/skill-library/qa-and-closure/overview.md)