SUMMARIZATION
Weekly slow-span brief with auto-filed Linear follow-ups
Weekly, ranks the top recurring slow-span clusters across services, writes an executive perf brief to Slack, and opens a Linear issue for each persistent regression that lacks one.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly end-of-sprint schedule fires
- ActionQuery recurring slow-span clusters ranked by time costHoneycomb
- ActionWrite executive perf briefOpenAI
- LogicFor each cluster, branch on existing Linear issueLinear
- ActionFile Linear issue for untracked regressionsLinear
- OutputPost brief with issue links to perf channelSlack
What it does
Each week this workflow surveys Honeycomb for the slow-span clusters that recurred most across the period, ranks them by total time cost, and produces an executive-readable performance brief. Beyond reporting, it acts: for every persistent regression without an existing ticket, it files a Linear issue with the narrative attached, so latency debt becomes tracked work rather than tribal knowledge.
When to use it
Use it when latency findings keep getting mentioned in Slack and then forgotten. This makes the weekly review automatic and guarantees the worst offenders turn into owned, prioritized Linear issues.
How it works
- 1A weekly schedule fires at end of sprint.
- 2The workflow queries Honeycomb for recurring slow-span clusters over the week, ranked by aggregate time cost.
- 3An LLM writes an executive perf brief summarizing the top regressions and trends.
- 4For each persistent cluster, it checks Linear for an existing matching issue.
- 5It branches: clusters already tracked are skipped; untracked ones get a new Linear issue with the narrative.
- 6The full brief, with links to any newly filed issues, posts to the perf Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect HoneycombDistributed traces and queries.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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