According to Andrej Karpathy on X, command line interfaces are a powerful bridge for AI agents because they are stable, scriptable, and natively accessible through terminal toolchains; he highlights that agents like Claude can install and use the new Polymarket CLI to generate custom dashboards, query markets, and automate logic within minutes (source: Andrej Karpathy, X/Twitter). As reported by Suhail Kakar on X, the Polymarket CLI is built in Rust and enables agents to query markets, place trades, and pull data with low overhead, positioning prediction markets as a first-class data and execution surface for agent workflows (source: Suhail Kakar, X/Twitter). According to Karpathy, pairing Polymarket CLI with GitHub CLI allows agents to navigate repositories, issues, PRs, and code, creating end-to-end autonomous pipelines from data ingestion to action (source: Andrej Karpathy, X/Twitter). For businesses, the opportunity is to make products agent-usable by providing markdown-exportable docs, publishing task-specific skills, and exposing functionality via CLI or Model Context Protocol to unlock automated growth loops and developer adoption (source: Andrej Karpathy, X/Twitter).

According to God of Prompt on X, Original Voices unveiled MCP-native human-in-the-loop audience research tools featuring 14 prebuilt workflows that can be deployed in minutes, with demos for ad creation and creative testing and an open-source skills library on GitHub (source: God of Prompt, links to Original Voices site, Loom demos, docs, and GitHub). According to the Original Voices skills documentation, the library provides modular Skills that connect models to real human feedback loops to reduce hallucinations and improve conversion-focused creative decisions. As reported by the GitHub repository for ovlabs Audience Research Skills, the project enables model-context protocol integrations, making it easier for teams to operationalize human validation in marketing workflows. For marketers and product teams, this suggests faster experimentation cycles, measurable lift in ad performance, and safer deployment of generative systems in production.

According to God of Prompt on X, Anthropic quietly released nine free Claude Skills tutorials covering Excel workflows, Chrome browsing, file editing, task automation, and project management, enabling beginners to build functional agents in under an hour (source: X post by @godofprompt, Feb 24, 2026). According to Andrew Ng on X, these Skills follow an open standard and can be deployed across Claude.ai, Claude Code, the Claude API, and the Claude Agent SDK, with instruction folders that equip agents with on‑demand knowledge and workflows (source: X post by @AndrewYNg, Feb 2026; deeplearning.ai course page). As reported by DeepLearning.AI, the short course “Agent Skills with Anthropic,” built with Anthropic and taught by Evan Schoppik, teaches best practices for creating custom skills for code generation and review, data analysis, research, and combining Skills with MCP and subagents to form agentic systems (source: deeplearning.ai/short-courses/agent-skills-with-anthropic). According to Anthropic’s Skills documentation referenced by Andrew Ng, businesses can standardize repeatable workflows across teams and products, lowering integration time by reusing the same Skills across multiple deployment surfaces, which creates near-term opportunities in ops automation, data reporting, and developer productivity.

According to bcherny, Anthropic’s Claude Code now supports installing Plugins, MCPs, and Skills to extend LSPs, agents, and custom hooks across major languages, with deployment via a team settings.json for auto-adding marketplaces; as reported by the linked Claude Code docs and Boris Cherny’s post, teams can install from the official Anthropic plugin marketplace or host a private company marketplace and start with the /plugin command (source: Boris Cherny on X; docs: Anthropic Claude Code documentation at code.claude.com/docs/en/discover-plugins). This creates business opportunities to standardize AI coding assistance, enforce enterprise governance on tool access, and scale language server protocol coverage across polyglot codebases, according to the same sources.

According to Claude (@claudeai) on X, Cowork is now available on Windows with full feature parity to macOS, including file access, multi-step task execution, plugins, and MCP connectors. As reported by the official Claude post, this cross-platform release enables Windows enterprises to operationalize autonomous multi-step workflows, integrate local and network file systems, and standardize plugin-driven automations across teams. According to the same source, MCP connectors expand interoperability with developer tools and enterprise systems, creating opportunities for IT to consolidate AI agents, reduce integration costs, and accelerate workflow automation proof-of-concepts on Windows fleets.

According to God of Prompt on Twitter, a new YouTube video has been released providing a comprehensive setup guide for MCP and AI agents. The guide covers essential steps for configuring MCP with AI agents, highlighting practical applications and workflow improvements for businesses and developers. As reported by God of Prompt, this video is aimed at helping professionals unlock the full potential of AI-driven agent architectures and streamline automation processes.

According to God of Prompt on Twitter, MCP (Model Context Protocol) has rapidly gained prominence as the leading interoperability standard in the AI industry, enabling seamless communication between AI agents and human oversight systems. Major industry players including Anthropic, OpenAI, and Google are actively building on MCP to address the challenges of multi-agent systems that lack coordination. This protocol is set to transform the development of multi-agent AI solutions, enhancing efficiency, safety, and oversight—critical factors for businesses deploying advanced autonomous systems (source: God of Prompt, Twitter, Jan 7, 2026).

According to Anthropic (@AnthropicAI), MCP has officially become part of the Agentic AI Foundation, a directed fund operated under the Linux Foundation. Co-creator David Soria Parra shared that MCP, initially developed as a protocol in a London conference room, is now recognized as an open standard for connecting AI systems to various external tools and platforms. This integration under the Linux Foundation is expected to accelerate the adoption of MCP in enterprise and open-source AI projects, creating new business opportunities for interoperability and ecosystem growth (source: AnthropicAI, Dec 11, 2025).