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debug-with-sentry

Investigate production errors using Sentry stack traces, which show the code execution path, and events leading up to the failure. Identify likely causes, triggering conditions, affected users, and releases before changing code.

OriginalShow the original description

Root-cause a production error with Sentry's evidence before touching code — pull the issue, read the stack trace and breadcrumbs, and separate the crash from its trigger. Use when the user pastes a Sentry link or issue ID, reports a production error, or asks why something is crashing for users.

Works withSentry
See how to install

Good for

  • Investigating an error from a Sentry link
  • Finding what triggers user crashes
  • Checking affected users and releases
  • Identifying the code that needs fixing

Files included (1)

  • SKILL.md3.2 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Root-cause with the evidence, not the guess

A Sentry issue is a claim about what broke. The job is to turn it into a claim about why it broke, backed by the event data, before anyone edits code.

1. Get the real issue, not the search hit

A pasted URL or short ID (PROJECT-123) goes straight to get_issue_details. A described error ("users can't check out") goes through search_issues first, but treat the results as candidates: match on the error type and the affected code path, not on title similarity. If two issues share a message but differ in stack trace, they are two bugs.

2. Read the stack trace like a diff

Fetch the newest event's stack trace. The top frame is where it crashed, not where it broke — walk down until you leave library code and hit the first frame owned by this repo. That frame and its local variables are the starting point. Cross-check the file against the working tree: production runs a release, and the line numbers may belong to code that has since changed. find_releases tells you which version the events come from.

3. Use the distribution before the theory

Before proposing a cause, check what the events have in common:

  • Tags (get_issue_tag_values): one browser, one endpoint, one tenant, one release? A bug scoped to a dimension names its own trigger.
  • Breadcrumbs: what the user did right before. A crash after a retry storm is a different bug than the same crash on first click.
  • First seen / volume: an issue that started with a release points at that release's diff. One that grew slowly points at data or traffic.

Seer (analyze_issue_with_seer) is worth running for a second opinion, but verify its suggested cause against the frames yourself before repeating it.

4. Report cause, scope, and blast radius

The finding is three sentences: what fails, why, and who it hits (volume, affected users, releases). Link the issue. If the user asks for a fix, fix the frame you identified — and only then consider update_issue to assign or resolve, never as a way to make the dashboard quiet.

Anti-patterns

  • Fixing the top frame. The top frame is often a library that faithfully threw on bad input from three frames below.
  • Trusting the issue title. Sentry titles are the exception message; two different bugs can share one, and grouping is heuristic.
  • Reading one event. One event is an anecdote; the tag distribution is the evidence.
  • Resolving without a shipped fix. A resolved issue that regresses comes back as a new-looking issue and erases its history.

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