Eval

"Unit tests" for your agent skills

Public results and task definitions for FrontierHarness Eval

Agent Skill evaluation harness for paired variants, trace artifacts, and runner adapters

Test that your Claude Code skills, MCP servers, and CLIs actually work when an agent uses them — sandboxed YAML suites, activation checks, A/B experiments, CI gates.

Multi-tier framework for evaluating AI agent skills with quality gates, semantic overlap detection, synthetic evaluation dataset generation, and live agent evaluation that measures how skills affect agent behavior.

An MCP server that autonomously evaluates web applications.

A Model Context Protocol (MCP) server that provides advanced code analysis and reasoning capabilities powered by Google's Gemini AI

build eval for your agent in 10 mins

开源 AI 应用评测平台，支持 RAG、AI Agent、多轮对话、LLM-as-Judge、接口评测、评测报告和人工盲测。Open-source AI evaluation platform for RAG, AI Agents, multi-turn conversations, LLM-as-Judge, endpoint evaluation

GitHub Copilot and AI Agent skill that parses job descriptions, mirrors exact technical terminology, quantifies impact metrics, and evaluates engineering compensation packages.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Comprehensive developer guide covering prompt engineering, agent orchestration, LLM application architecture, and evaluation formulas.

The job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.

Upstash Context7 MCP Server providing ultra-fast vector search, semantic context retrieval, and Redis caching for AI models and Cursor IDE.

Open-source AI-native canvas and agent workspace unifying multi-threaded chat, knowledge retrieval, and document co-creation.

Comprehensive hands-on course and curriculum for Large Language Models (LLMs) with interactive Colab notebooks, fine-tuning recipes, quantization, and evaluation roadmaps.

Improve how you work with AI. Portable Claude Code and Codex skill with evidence-based assessment and evaluation.

The job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.

Milvus & Zilliz-powered Claude Desktop and Cursor MCP server for long-term agent memory, doc retrieval, and code knowledge graph querying.

达尔文.skill —— 一个让你的Skill无限进化的系统：评估→改进→测试→保留或回滚 | Autoresearch-inspired autonomous skill optimization for Claude Code. Evaluate, improve, test, keep or revert.

Harness self-evolution: update targets, theory, evaluation, and reported evidence. Bilingual reading list and project website.

Learn AI Agents step by step, from scratch - from function calling to agent loops to multi-agent systems, orchestration, and evaluation.

Local streaming reverse proxy between AI coding agents (Claude Code, Cursor, Codex) and model APIs (Anthropic, OpenAI, Gemini, MiniMax). Meters every token + USD cost, compacts bloated context to cut pay-per-token API spend, and runs shadow-eval to prove quality held. ccusage-style metering + live local dashboard.

🛡️AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation

The operational superset of Pi Coding Agent — everything Pi, plus observability, governance, recovery, evaluation and multi-agent orchestration. Pi Coding Agent 的运维超集——完整继承 Pi 的核心能力，并进一步扩展可观测性、治理、故障恢复、评估和多 Agent 编排。