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Evaluating Agent System Performance

sickn33/antigravity-awesome-skills

Provides comprehensive guidance on building robust evaluation frameworks for complex agent systems. It addresses the inherent challenges of non-determinism and multiple valid paths, enabling systematic performance testing, validating context engineering choices, and measuring continuous improvements across multiple quality dimensions (e.g., factual accuracy, tool efficiency).

Free Tool Strategy

free-tool-strategy

coreyhaines31/marketingskills

Guides teams through engineering-as-marketing plans for free tools that generate leads, uplift SEO, and boost brand awareness, covering assessment, ideation, MVP scope, lead capture, SEO, build-vs-buy, and evaluation.

Langfuse Observability Suite

sickn33/antigravity-awesome-skills

Track and improve production LLMs with Langfuse observability: trace API calls, manage prompts, score evaluations, integrate with LangChain/LlamaIndex/OpenAI, and keep dashboards on cost, latency, and quality.

Langfuse LLM Evaluation And Benchmarking

langfuse-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive workflow for implementing robust LLM evaluation using Langfuse. This skill guides users through key processes including scoring traces (numeric, categorical, boolean), collecting user feedback, managing versioned prompts, creating structured datasets, running automated experiments, and implementing LLM-as-a-Judge patterns for advanced quality assurance and system benchmarking.

LangSmith LLM Observability

langsmith-observability

Orchestra-Research/AI-Research-SKILLs

LangSmith centralizes tracing, evaluation, and monitoring for LLM applications, letting teams debug prompts, run regression tests, collect latency/cost metrics, and integrate with OpenAI, Anthropic, LangChain, or LlamaIndex to keep production systems observable.

LLM Evaluation and Performance Testing

sickn33/antigravity-awesome-skills

Master comprehensive strategies for evaluating Large Language Model (LLM) applications. This skill covers automated metrics (BLEU, ROUGE, BERTScore), structured human evaluation dimensions, and advanced LLM-as-Judge techniques. Use it to systematically measure model performance, compare prompts, detect regressions, and build confidence in production systems.

EU MDR Medical Device Compliance Specialist

mdr-745-specialist

alirezarezvani/claude-skills

A comprehensive guide for achieving compliance with the EU MDR 2017/745. It covers crucial aspects including medical device classification (Annex VIII), developing technical documentation (Annex II/III), planning clinical evaluation (CER), and establishing post-market surveillance (PMS/PSUR) systems. Ideal for regulatory specialists preparing for Notified Body audits or needing to gap-check technical files.

Model Evaluation Metrics

model-evaluation-metrics

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for evaluating machine learning models, covering best practices, sample code, and validation patterns triggered by mentions of model evaluation metrics.

NeMo LLM Evaluator

nemo-evaluator-sdk

Orchestra-Research/AI-Research-SKILLs

Enterprise NeMo Evaluator SDK runs over 100 benchmarks from 18+ harnesses with multi-backend execution (Docker, Slurm, cloud) to give reproducible, scalable LLM evaluation workflows.

Phoenix AI Observability

phoenix-observability

Orchestra-Research/AI-Research-SKILLs

Phoenix is an open-source observability platform for tracing, evaluating, and monitoring LLM applications; use it to debug prompts, run dataset evaluations, build experiment pipelines, and watch production inference in real time.

PostHog Feature Flags And A/B Testing

posthog-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

Provides a comprehensive workflow for managing feature flags, running A/B/multivariate experiments, and analyzing user cohorts using PostHog. The guide covers implementing flag evaluation both client-side (browser) and server-side (Node.js), and demonstrates how to programmatically set up, track, and query experiment results via API calls (cURL/Bash).

Capturing Events with PostHog SDKs

posthog-hello-world

jeremylongshore/claude-code-plugins-plus-skills

This guide provides minimal working examples demonstrating the three core PostHog functionalities: event capture, user identification, and feature flag evaluation. It covers both server-side (Node.js, Python) and client-side (JavaScript) SDK usage, as well as direct API calls, making it perfect for initial integration setup, testing, and learning basic product analytics patterns.

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