monitoring-observability

name	monitoring-observability
license	MIT
compatibility	Claude Code 2.1.56+.
description	Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse LLM tracing, and drift detection. Use when adding logging, metrics, distributed tracing, LLM cost tracking, or quality drift monitoring.
tags	["monitoring","observability","prometheus","grafana","langfuse","tracing","metrics","drift-detection","logging"]
context	fork
agent	metrics-architect
version	2.0.0
author	OrchestKit
user-invocable	false
complexity	medium
metadata	{"category":"document-asset-creation"}

Monitoring & Observability

Comprehensive patterns for infrastructure monitoring, LLM observability, and quality drift detection. Each category has individual rule files in rules/ loaded on-demand.

Quick Reference

Category	Rules	Impact	When to Use
Infrastructure Monitoring	3	CRITICAL	Prometheus metrics, Grafana dashboards, alerting rules
LLM Observability	3	HIGH	Langfuse tracing, cost tracking, evaluation scoring
Drift Detection	3	HIGH	Statistical drift, quality regression, drift alerting
Silent Failures	3	HIGH	Tool skipping, quality degradation, loop/token spike alerting

Total: 12 rules across 4 categories

Quick Start

# Prometheus metrics with RED method
from prometheus_client import Counter, Histogram

http_requests = Counter('http_requests_total', 'Total requests', ['method', 'endpoint', 'status'])
http_duration = Histogram('http_request_duration_seconds', 'Request latency',
    buckets=[0.01, 0.05, 0.1, 0.5, 1, 2, 5])

# Langfuse LLM tracing
from langfuse import observe, get_client

@observe()
async def analyze_content(content: ):
    get_client().update_current_trace(
        user_id=, session_id=,
        tags=[, ],
    )
      llm.generate(content)