Introduction
If you've ever debugged a production incident, you know the pain. An alert fires in Slack. You jump to Sentry to read the stack trace. Then you switch to GitHub to check recent commits and open issues. By the time you've stitched the story together, twenty minutes are gone and you're juggling six browser tabs.
I wanted to fix that. So for the Pirates of the Coral-Bean Hackathon, I built CoralOps — an AI DevOps Command Center that pulls GitHub, Sentry, and Slack into one place and lets you investigate incidents with a single query and an AI-generated root cause report.
The core idea: One AI. All Signals. One Truth.
In this guide, I'll walk you through how CoralOps works and how you can build something similar using Coral's cross-source SQL — querying multiple data sources together like a single database.
What We're Building
CoralOps is a web dashboard that:
Connects GitHub (repos & issues), Sentry (errors & alerts), and Slack (channels & alerts)
Runs one cross-source SQL query to fetch signals from all three at once
Feeds the combined results to AI (Claude) to produce an incident summary, root cause, and recommended action
Displays everything in a clean command-center UI with metric cards, an investigation box, and an errors-over-time chart
The headline feature is the "Investigate" flow: you type a repo or service name, hit Investigate, and CoralOps returns an AI-powered root cause analysis drawn from all three sources — zero tab-switching.
Why Coral?
The hard part of a tool like this isn't the UI — it's the data. GitHub, Sentry, and Slack each have different APIs, auth models, and data shapes. Normally you'd write three separate integrations and a pile of glue code to join them.
Coral solves this by letting you treat each service as a queryable table. Instead of three API clients and manual joins, you write one SQL query that spans all your sources. That's what makes the "one query, one report" experience possible.
Prerequisites
Before you start, make sure you have:
A Coral account / environment set up
GitHub, Sentry, and Slack connected as data sources
Claude API access for the AI analysis layer
Basic familiarity with SQL and a web framework (the demo runs on localhost:8000)
Step 1: Connect Your Data Sources to Coral
The foundation of CoralOps is treating each external service as a source Coral can query. Register GitHub, Sentry, and Slack so Coral exposes them as tables — for example, GitHub issues, Sentry error events, and Slack alert messages.
Once connected, you no longer think in terms of "the GitHub API" or "the Sentry API." You think in terms of tables you can SELECT from and JOIN together.
step 2: Write a Cross-Source SQL Query
This is the magic step. Instead of three separate calls, you write a single query that pulls signals across sources. A simplified version of the kind of query CoralOps runs looks like this:
sql
SELECT service,
COUNT(*) AS errors,
AVG(duration_ms) AS avg_latency
FROM sentry_issues
WHERE timestamp > NOW() - INTERVAL '1 hour'
GROUP BY service
ORDER BY errors DESC;
From here you can extend the query to join in GitHub issues and Slack alerts so a single result set tells the whole story — which service is erroring, whether there's a recent open issue, and whether an alert already fired.
Step 3: Build the Dashboard UI
CoralOps surfaces the data through four headline metric cards:
GitHub Repos
Open Issues
Sentry Errors
Slack Channels
Above them sits the investigation input — a single text box where you enter a repo or service (e.g. RakeshSahoo-DS/hospital-patient-records) and an Investigate button. Below, an Errors Over Time chart visualizes the spike so you can see the incident at a glance.
Keep the layout focused: the goal is for a tired on-call engineer to understand the situation in a single screen.
Step 4: Add AI Incident Analysis with Claude
This is where CoralOps goes from "dashboard" to "command center." Take the cross-source query results and pass them to Claude with a prompt asking for three things:
AI Summary — a plain-language description of what's happening
Root Cause — the likely underlying issue, with a confidence score
Recommended Action — the concrete next step
Step 5: Wire Up the "Investigate" Flow
Now connect the pieces into one pipeline:
Search box → Cross-source SQL query → AI analysis → Rendered report
When the user clicks Investigate, CoralOps runs the Coral query, hands the results to Claude, and renders the summary, root cause, recommended action, and cross-source evidence — all from one click.
Conclusion
Building CoralOps taught me how powerful it is to treat disparate tools as one queryable database. The real DevOps problems — context switching, scattered alerts, slow incident response — all come down to data living in silos. Coral's cross-source SQL collapses those silos into a single query, and AI turns that query into an actual answer.
That's the whole philosophy behind CoralOps: One AI. All Signals. One Truth.
If you're building developer tooling, try unifying your sources with Coral first — it changes what's possible. Thanks for reading, and happy hacking, pirates! 🏴☠️
