本文来自PRO会员通讯内容，文末关注「机器之心PRO会员」，查看更多专题解读。 目录 01. builder 比 user 还多，MCP 仅是「旧瓶装新酒」？ 一年过去，社区对于 MCP 的定位仍有争议？平均 25 个用户对应 1 个开发者，MCP 目前更多是开发者自娱自乐的产物？... 02 . Not Skills vs MCP, but Skills with MCP? 「人如其名」，Skills 真是来 kill MCP 的？MCP 能做但 Skills 不能做的，现在也没什么用？... 03 . 过去一年，围绕 MCP 的 infra 层格局逐渐清晰？ MCP 大规模落地还得看下一个「微信小程序」入口的出现？... builder 比 user 还多，MCP 仅是「旧瓶装新酒」？ 引言： 近期，Anthropic 新推出的 Claude Skills 在社区内收获了相对一致的好评，被不少开发者视为「终于能直接拿来用」的能力；几乎同一时间，MCP 协议的「一周年纪 念日」却在一片「寂静」中度过。实际上从发布以来，MCP 的「builder 多于 user」、只是「旧瓶装新酒」的质疑始终存在，而在 Sk ...

Core Insights - Claude Skills represents a significant advancement in AI Agent capabilities, allowing for dynamic discovery and loading of specialized knowledge, transforming general agents into task-specific experts [8][4] - The underlying design philosophy of information layering is a key breakthrough that enhances token efficiency by up to 95%, improving decision quality and response speed [6][9] Information Layering Design - Information layering allows agents to process complex tasks efficiently by first accessing an index, then a summary, and only retrieving the original content when necessary [5][6] - This design philosophy is akin to techniques used in 3D game development, such as Level of Detail (LOD) and on-demand loading, which optimize resource usage [12][20] Three-Layer Architecture - The three-layer architecture consists of: - LOD-0: Summary Layer, providing minimal metadata for quick browsing [29] - LOD-1: Core Layer, offering essential information sufficient for 80-90% of routine tasks [30] - LOD-2: Raw Layer, containing complete information for in-depth analysis when needed [31][32] - This structure enables agents to efficiently navigate vast information landscapes, reducing token consumption and improving operational speed [60] Practical Application - In a case study analyzing quarterly performance, agents utilize LOD-0 to identify relevant data assets, LOD-1 to generate high-quality summaries, and LOD-2 for detailed queries, demonstrating the architecture's effectiveness [51][56] - The results show a dramatic reduction in token consumption from approximately 150,000 to 5,000, and a significant decrease in response time from 45 seconds to 5 seconds [60] Challenges and Considerations - Implementing an information layering architecture requires substantial initial investment in creating high-quality LOD-1 summaries and maintaining synchronization across layers [63][64] - The complexity of designing a layered system necessitates careful consideration of information scale, frequency of updates, and access patterns to avoid over-engineering [66] Universal Principles - The core principles derived from Claude Skills emphasize using metadata instead of complete information and adopting on-demand loading strategies to optimize resource usage [67][71] - These principles can be applied across various information-intensive systems, enhancing efficiency and intelligence in agent design [85]

Core Insights - The article discusses the recent advancements in "Agent Tooling" by major AI companies like OpenAI, Google, and Anthropic, highlighting the growing importance of these tools in leveraging AI capabilities effectively [6][7][11]. Group 1: Developments in Agent Tooling - OpenAI launched AgentKit, a comprehensive tool for developers to create and manage AI agents, which includes features for building, deploying, and maintaining agents [12][18]. - Google introduced Gemini CLI Extensions, enhancing its Gemini ecosystem, while Anthropic released Claude Skills, allowing users to define workflows without programming [6][7]. - The rapid evolution of agent tools is driven by the increasing capabilities of AI models, with significant upgrades occurring more frequently [8][26]. Group 2: Market Opportunities and Trends - The global developer tools market is estimated to be around $20 billion to $30 billion, with AI potentially increasing this market size tenfold [9][50]. - Companies like LangChain and ElevenLabs have recently achieved significant valuations, indicating strong investor interest in the agent tooling space [7][9]. - The article suggests that the market for agent tools could reach $200 billion to $500 billion, driven by the transformation of service industries through AI [50][51]. Group 3: Investment and Entrepreneurial Landscape - AGI House has invested in over 20 companies in the agent tooling space, reflecting a strategic focus on early-stage investments in this rapidly evolving sector [8][9]. - The emergence of companies like Composio, which integrates high-quality MCP servers, showcases the entrepreneurial opportunities within the agent tooling ecosystem [30][34]. - The article emphasizes the potential for large companies to emerge in this space, with examples of existing companies achieving substantial revenues [51][52]. Group 4: Technological Evolution and Future Directions - The article outlines six major evolutions in agent tooling, emphasizing the need for tools that can support complex operations as AI capabilities advance [23][26]. - Future developments are expected to focus on enhancing reasoning, tool usage, and voice capabilities, with a trend towards deeper integration of multimodal functionalities [28][40]. - The concept of memory in agents is highlighted as a critical area for development, with companies like Letta exploring innovative memory solutions for agents [42][44].

Core Insights - Generative AI is significantly reshaping various industries, with advancements in custom chips, medical breakthroughs, and governance laws highlighting both opportunities and challenges in the sector [1][4][19] Company Developments - Microsoft launched its first in-house image generator, MAI-Image-1, which aims to reduce generic styling and improve photorealistic scene generation, positioning itself to diversify beyond OpenAI [7][10] - xAI, founded by Elon Musk, is developing "world models" for video games and robotics, indicating a shift towards more complex AI systems capable of understanding physics-rich environments [6][8] - OpenAI has partnered with Broadcom to enhance its computational power, while also exploring adult-content AI applications, which has raised ethical concerns [4][10] - Google has updated its AI Studio and introduced new tools like Veo 3.1 and Flow, focusing on faster prototyping and enhanced video editing capabilities [11][12] - Anthropic introduced Claude Sonnet 4.5 and Claude Skills, emphasizing long-duration focus and customization for AI applications, which could redefine how AI is integrated into workflows [15][16] Industry Trends - The AI sector is witnessing a significant increase in electricity demand due to data center expansions, with projections indicating that AI could account for 6.7% to 12% of U.S. electricity consumption by 2028 [24][28] - The U.S. government has approved Nvidia's sale of advanced AI chips to vetted projects in the UAE, balancing national security with market demand [21][22] - California has become the first state to regulate AI companion chatbots, setting a precedent for ethical standards in AI interactions [22][23] - The competitive landscape is shifting towards physical infrastructure, with Nvidia, Microsoft, xAI, and BlackRock's $40 billion acquisition of Aligned Data Centers marking a strategic move to secure AI compute resources [25][28] Research and Development - AI-designed viruses have been developed to combat antibiotic-resistant bacteria, showcasing the potential of AI in medical research [32] - Large language models are increasingly being integrated into clinical trials, highlighting the need for human oversight and quality control in AI applications within healthcare [32][30] Regulatory Environment - Fed Governor Waller has warned about the potential risks of AI in financial markets, urging banks to implement risk controls before deploying generative models [19][22] - New governance laws are emerging to address ethical concerns surrounding AI, particularly in the context of adult content and emotional manipulation [19][20]

This video is brought to you by Stack AI. More on them later. GPT6 might be coming by the end of the year. This guy on CNBC said he just got done talking to Brad Gersonner, a prominent figure in Silicon Valley, and he just said GPT6 is coming by the end of this year.That's 2 and 1/2 months from now. Now, that comes right on the heels of GPT5. And honestly, I don't think it's going to be happening.It would be very weird to have this massive launch GPT5 really a fundamental shift in the way users interact wit ...

Core Insights - Anthropic has launched Claude Skills, a new mode that allows its model to acquire new functionalities through the use of Markdown files containing instructions, scripts, and resources [1][3][6] Summary by Sections Skills Overview - Skills are organized folders containing a SKILL.md file that provides instructions for agents to perform additional functions [3] - The new document generation feature of Claude is implemented through Skills, which now includes support for .pdf, .docx, .xlsx, and .pptx files [3][6] Practical Application - An example of a skill, slack-gif-creator, is designed to create GIFs optimized for Slack, including size validation [4] - The process of generating a GIF using the slack-gif-creator skill is straightforward, with the model checking file size to ensure it meets Slack's requirements [8] Technical Implementation - Skills rely on the model's ability to access the file system and execute commands in a coding environment, distinguishing them from previous large model extensions [9] - The implementation of Skills allows for easy iteration and improvement, making it a powerful tool for automating tasks [6][9] Comparison with MCP - Skills are seen as a more efficient alternative to the Model Context Protocol (MCP), which has limitations such as high token consumption [14] - Unlike MCP, Skills allow for direct task execution through simple Markdown files, reducing the need for extensive token usage [14][17] Future Potential - The potential for Skills is vast, with expectations for a significant increase in the number of Skills available, both as single files and more complex folders [15][16] - Skills can be integrated with other models, enhancing their functionality and usability across different platforms [15] Simplicity and Effectiveness - The simplicity of Skills is highlighted as a key advantage, allowing for easy implementation and execution without the complexity of traditional protocols [17] - Skills focus on providing text-based instructions that the model can interpret and execute, aligning with the essence of large models [17]

Core Insights - Anthropic has launched Claude Skills, a new mode that allows its model to acquire new functionalities through the use of organized folders containing instructions, scripts, and resources [2][5][12] Summary by Sections Skills Overview - Skills are essentially Markdown files that instruct the model on how to perform specific tasks while allowing for additional documentation and pre-written scripts [4][5] - The new document generation feature of Claude is implemented through Skills, enabling the model to handle various file formats like .pdf, .docx, .xlsx, and .pptx [4][5] Functionality and Implementation - Claude can improve its task execution by loading relevant Skills only when necessary, which enhances efficiency [5][6] - At the start of a session, Claude scans all available Skill files and reads brief descriptions from the YAML front matter, minimizing token usage [6] Practical Application - An example of a Skill is the slack-gif-creator, which generates GIFs optimized for Slack, demonstrating the practical utility of Skills in real-world applications [7][10] - Skills are designed to be easily shared, with simpler Skills potentially implemented as single files and more complex ones as folders [21][24] Comparison with MCP - The Model Context Protocol (MCP) has shown limitations, particularly in token consumption, which can hinder the model's effectiveness [18][20] - Skills offer a more efficient alternative, allowing for task completion without the extensive token usage required by MCP [20][24] Future Potential - The potential for Skills is vast, with possibilities for creating a "data journalism agent" that can analyze and publish census data using just a folder of Markdown files and Python scripts [16][19] - Skills are expected to lead to a significant expansion in the ecosystem, surpassing the previous excitement surrounding MCP [24] Design Philosophy - The simplicity of Skills is a key advantage, allowing for straightforward implementation without the complexity of full protocols like MCP [25][27] - Skills focus on leveraging the model's capabilities to solve problems with minimal input, aligning with the essence of large models [27]