CUSTOMER SUPPORT

Weekly bug-report repro quality report from logged ticket scores

On a weekly schedule, reads all bug-ticket repro scores logged to Postgres, computes completeness trends and the most common missing fields.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule fires
  • ActionQuery past week's repro scoresPostgreSQLPostgres
  • ActionSummarize trends and top gaps (LLM)OpenAI
  • ActionPost quality report to SlackSlack
  • OutputFile Linear issue with recommendationsLinearLinear

What it does

This workflow turns individual repro scores into a team-level signal. It queries a Postgres table where each bug ticket's repro score and missing-field list were logged, aggregates the week's data, and produces an LLM-written report covering average completeness, week-over-week trend, and the fields customers most often leave out. The report goes to Slack and is filed as a Linear issue so improvements get owned.

When to use it

Use it when you already score incoming bugs and want a recurring read on whether report quality is improving and which prompts or form fields to fix.

How it works

  1. 1A weekly schedule triggers the run.
  2. 2A Postgres query pulls the past week's repro scores and missing-field records.
  3. 3An LLM summarizes the aggregates into trends, top missing fields, and concrete recommendations.
  4. 4The narrative report is posted to the support leadership Slack channel.
  5. 5A Linear issue is created capturing the recommendations so process changes get tracked to completion.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect SlackChannels, DMs, threads, mentions.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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