Your browser agent works in a demo. How does it handle 200 web tasks? Browser Use Benchmark is a public repository of browser-agent evaluations for builders testing web automation. It helps you move beyond spot checks by running BU Bench V2.1 tasks against weighted scoring rubrics, with saved evidence to inspect what happened. Key features: • 200-task evaluation – the default runner loads the full V2.1 task set • Partial-credit scoring – weighted rubrics capture more than a pass/fail result • Inspectable evidence – saved traces, screenshots, and task outputs help you investigate failures • Cloud or local browsers – run with Browser Use Cloud or your own Chrome/Chromium • Targeted test runs – select specific task IDs and adjust concurrency to fit your API limits Free public GitHub repo. Running evaluations requires your own API keys. Compare scores only on the same task set and revision. Link in the reply 👇

Running several agents gets messy fast. Raven is built to coordinate them. Raven is a pre-alpha multi-agent orchestration repo for builders who want to coordinate specialized agents from one place. It helps you delegate work across research, coding, design, and unattended workflows by bringing built-in and third-party agents into shared workflows. Key features: • Unified orchestration surface – delegate tasks, coordinate execution, and integrate results in one place • Four built-in agents – Raven-Research, Raven-Code, Raven-Design, and Raven-Oncall cover distinct workflow types • Deep research support – Raven-Research produces structured reports with traceable sources for complex questions and technical analysis • Agentic development workflows – Raven-Code supports implementation, debugging, refactoring, data processing, and analysis • Third-party agent connections – connect external agents and coordinate their capabilities in shared workflows It’s open-source under the Apache License 2.0. Link in the reply 👇

Your finance agent needs skills, not another pile of scripts. Awesome Finance Skills is a public GitHub collection of plug-and-play finance skills for LLM agents working on market/news analysis. It helps you add financial context faster by packaging separate skills for news, stock data, sentiment, prediction, signal tracking, visualization, reporting, and search, with npx and manual install paths documented in the README. Key features: • Skill-based setup – install individual skills with npx skills or copy the skills folder manually • News + trends – alphaear-news aggregates financial news/trends from 10+ sources including Cailian, WSJ, Weibo, and Polymarket • Market data coverage – alphaear-stock covers A-share, HK, and US stock data with ticker search, OHLCV, and fundamentals • Analysis helpers – sentiment scoring, Kronos forecasting, and signal tracking are split into separate agent skills • Output workflows – logic-chain diagrams, report generation, and search/RAG skills give agents more ways to explain results It’s open-source under the Apache License 2.0. Link in the reply 👇