Hud Power for Kiro

Production code runtime sensor for monitoring application performance, investigating issues, and analyzing code behavior in real-time. This power enables Kiro AI agents to query and analyze your production systems using Hud's data.

About Hud

Hud is the first Runtime Code Sensor designed specifically to make AI-generated code production-safe by default. Installed in under a minute with no configuration needed, it streams real-time, function-level runtime data from your live systems directly into your IDE.

Once deployed, Hud tracks how each function behaves in production—mapping execution patterns, surfacing behavioral changes, and delivering structured runtime signals without requiring logs or any manual work.

Hud is purpose-built for engineering teams using code-generating agents like Kiro.

Features

• Real-Time Production Insight: Monitor function-level performance metrics, execution patterns, and errors
• Forensics: Detailed execution traces collected for specific requests to help with root cause analysis
• Zero Configuration: Deploy in under a minute with no configuration needed
• AI-First Design: Built specifically for AI agents to reason over production behavior
• SQL-Based MCP: Query your production runtime data from Hud using SQL through MCP tools
• Smart Agent Context Management: Pre-built investigation workflows for common scenarios (endpoint errors, slow endpoints, deployment analysis)

Installation

Prerequisites

1. Hud Account: Sign up at app.hud.io
2. Hud SDK: Install the Hud SDK in your production services (Node.js or Python)

Kiro Power Setup

Install Hud IDE Extension

1. Install the "Hud" extension from your IDE marketplace
2. Login via the extension panel

Add Hud Kiro Power

1. Open the Powers panel
2. Click "Add power from GitHub"
3. Enter: https://github.com/code-hud/hud-kiro-power

What can you do with Hud and Kiro?

1 - Investigate Production Errors

Use prompts like:

• Why is /endpoint failing in production?
• Why is kafka_queue returning errors since 10:00?
• Why does /api return 501?
• Why does service service have TypeError?

2 - Analyze Deployment Impact

Use prompts like:

• Did my deployment from 14:00 cause any performance degradation?
• Is this week's performance improved compared to last month?
• Make sure no deployments from the last week caused a higher error rate

3 - Debug Slow Endpoints

Use prompts like:

• Why is /api slow in production?
• Users are complaining that sometimes the product pages take a minute to load, why?
• What's the endpoint with the highest P90 in my_service? Why is that happening?

Resources

• Documentation: docs.hud.io
• Support Email: [email protected]
• Support Chat: app.hud.io