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MIT-licensed Agent Skills collection for context engineering, harness engineering, multi-agent architectures, filesystem context, memory systems, tool design, evaluation, hosted agents, and production agent operating loops for Claude Code, Cursor, Codex, and Open Plugins-compatible agent tools.

Official Hugging Face Agent Skills collection for Claude Code, Codex, Cursor, Gemini CLI, and other skills-compatible agents, covering Hub CLI workflows, datasets, model search, Spaces, Gradio, fine-tuning, evaluations, local models, papers, Trackio, ZeroGPU, transformers.js, TRL, and the Hugging Face MCP server.

MCP server for generating academic diagrams, statistical plots, figure packages, and visual evaluations from research context through PaperBanana's multi-agent illustration pipeline.

Apache-2.0 library for loading, sharing, streaming, inspecting, and preprocessing AI datasets from the Hugging Face Hub or local files.

Apache-2.0 library for loading, computing, comparing, saving, and sharing evaluation modules for machine learning models and datasets.

Open-source LLMOps platform for prompt management, prompt versioning, evaluation, and observability across LLM applications.

Open-source Python framework for unit-testing LLM applications, agents, RAG pipelines, metrics, regression suites, and traces.

Open-source data labeling, annotation, and human-in-the-loop AI evaluation platform for text, images, audio, video, time series, and multimodal datasets.

Open-source evaluation framework for testing RAG systems, prompts, agents, workflows, and other LLM application behavior.

Open-source LLM engineering platform for tracing, prompt management, evaluation, metrics, and observability.

Slash command runbook for designing and running prompt evaluations: define tasks, success criteria, golden outputs, regression checks, and privacy-safe reporting using Anthropic test-and-evaluate guidance.

Open-source observability and evaluation tooling for LLM applications, traces, datasets, and experiments.

Evaluation, prompt experimentation, logging, and data platform for production AI application development.

Observability, evaluation, tracing, and testing platform for LLM applications and agent workflows.

Weights and Biases toolkit for tracking, evaluating, and debugging LLM applications and agent workflows.

scikit-learn ML modeling rule that audits for data leakage, enforces Pipeline-based preprocessing, and validates cross-validation rigor (StratifiedKFold, GroupKFold, TimeSeriesSplit) for defensible model evaluation

Open-source suite of development tools from Microsoft for building LLM applications end to end — create executable flows that link LLMs, prompts, Python, and tools, trace and debug them, evaluate quality against datasets in CI/CD, and deploy to a serving platform.

Inspect LLM traces and spans, manage prompts, explore datasets, and review evaluation experiments from Claude — with the official Arize Phoenix MCP server, built into the open-source Phoenix AI observability platform.

Connect Claude to LangSmith — retrieve conversation threads and traces, fetch and push prompts, browse evaluation datasets and experiments, and access billing usage — with the official LangSmith Model Context Protocol server from LangChain.

Debug, evaluate, and monitor LLM applications from Claude — read traces and spans, score outputs, save prompts, run evaluation experiments, and query project metrics — with the official Opik MCP server by Comet.

Open-source ML and LLM observability framework for evaluating, testing, and monitoring data quality, drift, model behavior, and AI application outputs.

Open-source AI engineering platform for tracing, evaluating, prompt-managing, and deploying agents, LLM applications, and ML models.

Open-source evaluation and tracing framework for measuring AI agents, RAG systems, LLM apps, retrieval quality, feedback metrics, and trace-level regressions.

AI testing platform for evaluating, scanning, and monitoring machine learning and LLM application quality.

Capability comparison of Claude Code, Amazon Q Developer (formerly CodeWhisperer), and Google Gemini Code Assist: form factor, agentic vs completion, IDE support, cloud ties, and free tiers.

Capability comparison of Claude Code, Cursor, and Windsurf (formerly Codeium): form factor, where each runs, agentic vs autocomplete, MCP extensibility, and free tiers, grounded in each tool's official docs.

Capability comparison of Claude Code, GitHub Copilot, and ChatGPT (Codex) for Python development. Form factor, where each runs, agentic vs inline, IDE integration, MCP, and free tiers, grounded in each tool's official docs.

Source-backed agent for designing Inspect AI benchmark tasks, datasets, solver plans, scorer rubrics, model matrices, eval logs, and release-quality prompt evaluation decisions.

Open-source framework from OpenAI for evaluating LLM and agent behavior with reusable eval definitions, grading logic, datasets, and regression workflows.

A practical guide for comparing AI coding tools with repeatable benchmarks, fixed task sets, controlled environments, transparent scoring, and privacy-safe artifacts.