Nobody reads raw log streams until something is already on fire. This automation watches your error logs every hour and turns thousands of lines into a short, readable brief.
sonnet3 daysDatadogSentryOpenAISlack
Claude
81ROI
80Scale
$2.4k89Saved
ROI for
README.md
Why this subagent
Nobody reads raw log streams until something is already on fire. This automation watches your error logs every hour and turns thousands of lines into a short, readable brief.
It groups repeated stack traces, counts how often each fault hit, and notes when it first showed up. You get a plain-language summary in Slack every hour, plus a stored history you can scan for slow-building trends.
How it runs
Used at step 01 to kick off the pipeline.
Write
Used at step 01 to kick off the pipeline.
WebFetch
Used at step 01 to kick off the pipeline.
WebSearch
Used at step 01 to kick off the pipeline.
Group similar stack traces together so a single recurring fault does not flood the report.
pending
Write a plain-language summary naming the top failures, their counts, and the first time each appeared.
pending
Post the summary to a Slack channel and store the raw grouping in Postgres for trend lookups.
pending
Sample output
json
// Sample output
// (generated when the pipeline finishes)
Read the grouped error logs and return a Slack-ready summary: top 5 issues by count, each with service name, occurrence count, first-seen timestamp, and a one-line probable cause.
Unlock the rest
The full agent definition, install snippet, and starter task are gated for community members.
Members get the full `.md` agent file, the npm / pnpm install one-liners, a starter prompt that we've tuned against real runs, and the open-source repo when this automation ships there. One email, magic link, done.