Ship more. Toil less.
Code review, debugging, docs, on-call runbooks. Engineers get every model and every tool in one workspace — without juggling subscriptions.
A staff engineer at a series-B startup shipped a PR-review agent in one afternoon — GitHub MCP, custom playbook, Claude Opus. PR review time dropped from 4 hours to 40 minutes per merge.
Bring your repo to the chat.
Drop in a file, a directory, or a PR diff. Ask Wirebase to review, refactor, or explain. With MCP servers wired to GitHub, Linear, and your CI, the model sees the same context you do — and acts on it.
- Drag-and-drop code files and diffs
- GitHub MCP server for PRs, issues, comments
- Linear and Jira MCP for tickets
- Custom MCP servers for internal tooling
From stack trace to fix.
Paste a stack trace, an error log, or a failing test. Wirebase walks through the likely causes and suggests a patch. Connect to your observability tools via MCP for live system context.
- Stack trace and log analysis
- Diff suggestions you can apply
- Sentry/Datadog/Honeycomb MCP integration
- Postmortem drafting with timeline reconstruction
Runbooks that update themselves.
Store runbooks in a knowledge base. When the on-call gets paged, they ask Wirebase what to check. Citations point to the source doc. As systems change, you update the doc, and the answers update with it.
- On-call runbook knowledge base
- PagerDuty MCP for incident context
- Cited answers — trust but verify
- Postmortem generation with timeline + actions
# Bring AI into your existing CI
- name: AI Review
uses: wirebase/action-review@v1
with:
api-key: ${{ secrets.WIREBASE_KEY }}
agent: pr-reviewer
files: ${{ github.event.pull_request.changed_files }}Daily wins, not flashy demos.
PR review
Style, correctness, edge cases. Inline suggestions.
Bug triage
Stack trace → likely cause → suggested patch.
Doc generation
README, API docs, migration guides.
Codebase search
Semantic search over your own repos.
CI workflows
Custom AI steps in your build pipeline.
On-call assist
Runbook answers with citations.