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Search LLM Monitoring , found 8 results

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Agent Evaluation

agent-evaluation

sickn33/antigravity-awesome-skills

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring\u2014where even top agents achieve less than 50% on re...

Groq Reference Architecture

groq-reference-architecture

jeremylongshore/claude-code-plugins-plus-skills

Defines best-practice Groq deployment with tiered model routing, middleware, streaming pipelines, and fallback chains for ultra-fast LLM inference and production monitoring when launching new Groq integrations.

Groq Webhook Event Patterns

groq-webhooks-events

jeremylongshore/claude-code-plugins-plus-skills

Build secure webhook endpoints around Groq's LLM API, covering streaming SSE, batch callbacks, webhook processors, and health monitoring while validating webhook signatures and handling Groq notifications for real-time workloads.

Langfuse Rate Limit Guards

langfuse-rate-limits

jeremylongshore/claude-code-plugins-plus-skills

Implements Langfuse rate limiting best practices—optimized batching, exponential backoff, concurrency caps, sampling, and header-aware monitoring—to keep trace ingestion steady under high-volume LLM workloads and react to 429/5xx throttling.

LangSmith Observability Platform

langsmith-observability

Orchestra-Research/AI-Research-SKILLs

LangSmith is an observability platform for debugging, evaluating, and monitoring LLM applications, combining tracing, dataset-backed evaluation, and production metrics to streamline AI testing and incident response.

🤖 LLM Application Patterns

llm-app-patterns

sickn33/antigravity-awesome-skills

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, buildin...

Phoenix AI Observability

phoenix-observability

Orchestra-Research/AI-Research-SKILLs

Phoenix is an open-source AI observability and evaluation platform that captures traces, experiments, datasets, and real-time dashboards for debugging, evaluating, and monitoring production LLM systems.

Production ML Engineering Guide

senior-ml-engineer

alirezarezvani/claude-skills

Guides ML engineers through deploying models, building MLOps pipelines, integrating LLMs, implementing RAG systems, and monitoring drift while emphasizing production readiness, cost control, and operational reliability.

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