ai-observability

Instrument and monitor AI interactions in Spring Boot applications with telemetry.

203|35|Updated Apr 14, 2026

Search Tags:#monitoring #prometheus #spring-boot #micrometer #token-usage #ai-observability

Ready to run

npx skills add https://github.com/rrezartprebreza/spring-boot-skills --skill ai-observability

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-observability
Source: https://github.com/rrezartprebreza/spring-boot-skills/tree/main/skills/ai-observability
Command: npx skills add https://github.com/rrezartprebreza/spring-boot-skills --skill ai-observability

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables comprehensive observability for AI integrations in Spring Boot projects. It provides instrumentation for AI calls, token usage, latency, and cost estimation, and supports auditing prompts/responses to improve reliability and governance.

Core Features & Use Cases

Built-in observability: Micrometer/Prometheus integration for generation latency, token usage, and AI client metrics.
Custom AI metrics and auditing: track latency, tokens, and per-model usage with structured logs.
Cost estimation: estimate AI usage costs based on per-model pricing.
Audit logging: persistent audit logs for AI interactions with metadata.
Configuration and gotchas: guide for enabling endpoints, tracing, and safety considerations.

Quick Start

Add the Micrometer dependencies, enable metrics exposure in your Spring Boot config, and wire AiMetrics into your AI calls to start collecting observability data.

Frequently Asked Questions about ai-observability

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I monitor token usage and latency in Spring Boot AI applications?▼

You can monitor token usage and latency in Spring Boot AI applications by wiring Micrometer and Prometheus into your AI calls. This Skill provides built-in instrumentation to track generation latency, token consumption, and AI client metrics automatically.

Can I estimate AI API costs per model using Micrometer and Spring Boot?▼

Yes, you can estimate AI API costs per model using this Skill's custom cost estimation feature. It calculates usage costs based on per-model pricing and exposes them alongside your standard Micrometer and Prometheus metrics.

How do I set up audit logging for Spring AI chat interactions?▼

Setting up audit logging for Spring AI chat interactions involves configuring the provided Java and Spring components to persist prompts and responses. This Skill generates persistent audit logs with metadata to improve reliability and governance.

Does this observability Skill work with existing Prometheus monitoring setups?▼

Yes, this observability Skill works directly with existing Prometheus setups by relying on Micrometer integration. You simply add the Micrometer dependencies and enable metrics exposure in your Spring Boot YAML configuration to expose the endpoints.

What is the best way to track per-model token usage in Spring AI evaluation pipelines?▼

The best way to track per-model token usage in Spring AI evaluation pipelines is to apply custom AI metrics and auditing. This Skill instruments model calls to capture structured logs detailing latency, tokens, and specific model usage.

Are there configuration gotchas when enabling tracing for AI observability?▼

Yes, there are configuration gotchas when enabling tracing for AI observability. This Skill provides guidance for safely exposing management endpoints, configuring tracing, and handling safety considerations specific to Spring Boot integrations.