SUMMARIZATION
Translate a Honeycomb slow trace into a plain-English Slack story
On demand, pulls a single slow Honeycomb trace by ID, turns its span waterfall into a plain-English latency story a non-engineer can follow, and posts it to Slack.
How it runs
The automated pipeline, trigger to output.
- TriggerOperator submits a Honeycomb trace ID and Slack channel
- ActionFetch the full span waterfall for the traceHoneycomb
- LogicRank spans by self-time and compute the critical path
- ActionRewrite the waterfall as a plain-English latency storyOpenAI
- OutputPost the narrative with trace link to SlackSlack
What it does
Takes one slow Honeycomb trace and rewrites its raw span waterfall into a short, jargon-free narrative: which step ate the time, how long the user waited, and what was happening underneath. The result lands in a Slack channel so support, sales, and execs can understand an incident without reading a flame graph.
When to use it
Use it when a customer complains 'the app was slow at 2pm' and someone drops a trace link in Slack. Instead of an engineer hand-explaining the waterfall, the operator pastes the trace ID and gets a clean summary anyone can read and forward.
How it works
- 1An operator triggers the workflow with a Honeycomb trace ID and target Slack channel.
- 2Honeycomb returns the full span list for that trace: names, durations, parent-child links, and service tags.
- 3A logic step sorts spans by self-time and finds the critical path — the chain that actually held up the response.
- 4OpenAI rewrites the waterfall as a timeline narrative: total wait, the one or two steps that dominated, and a plain analogy for the bottleneck.
- 5The narrative posts to Slack as a tidy message with the trace link attached for engineers who want the detail.
Set it up
What you configure once, before turning it on.
- 1Connect HoneycombDistributed traces and queries.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
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