Author: Liu Jun
In 2026, a consensus is forming in the AI industry: model capability is no longer the bottleneck. The gap lies outside the model—in the encoding of domain knowledge, in the interface between agents and the real world, in the maturity of toolchains. This gap is being filled by the open-source community, and the speed exceeds everyone's expectations. OpenClaw gained 60,000 GitHub stars within 72 hours, surpassing 350,000 three months later. The Claude Code Skill ecosystem grew from 50 to over 334 Skills within half a year. Hermes Agent is even more radical, enabling agents to autonomously build reusable skills. Data from Vela Partners shows that in the past 90 days, the combined categories of personal AI assistants and Agentic Skill plugins added 244,000 new stars. This is a Skill explosion.
Perseus Yang's work sits at the heart of this explosion. With a background in Mathematics and Computer Science from Cornell, a member of the Forbes Business Council, and a THINC Fellowship recipient, he has participated in and maintained over a dozen AI-related open-source projects on GitHub in recent years, covering areas such as agent skill expansion, mobile device-level control, AI engine optimization toolchains, GEO data analysis agents, content automation workflows, and payment protocol infrastructure. His characteristic is possessing both a deep engineering background and strong product intuition. He doesn't just write code; he defines what a tool should look like based on user needs, then builds it end-to-end and drives its adoption.
Here are several core judgments he has formed during this process.
First Judgment: The Skill System is the Most Underestimated Infrastructure in the AI Agent Era
After Anthropic released Agent Skills as an open standard at the end of 2025, OpenAI's Codex CLI also adopted the same SKILL.md format. OpenClaw's ClawHub registry has accumulated over 13,000 community-contributed Skills, and the Claude Code ecosystem is quickly following suit. The significance of Skills goes far beyond "adding plugins to agents." It essentially enables people who don't know how to code to participate in AI programming. An operations personnel can write a SKILL.md in natural language, enabling an agent to learn a new workflow. This is a paradigm shift: the true power of AI depends not on the model's parameter count, but on what domain knowledge is injected into the model, and Skills extend the power to inject knowledge from engineers to everyone.
But Perseus observed a problem. The vast majority of Skills are concentrated in the engineering field—code review, front-end design, DevOps, testing. Expertise in non-engineering fields has hardly been systematically encoded into Skills. This means the coverage of the Skill ecosystem is far from reaching its potential boundary.
This observation drove a series of his open-source work in the GTM (Go-To-Market) toolchain direction. The most representative is GTM Engineer Skills, a set of Claude Code and Codex skill sets covering the complete workflow of AI engine discoverability, which has accumulated over 600 stars on GitHub. It encodes work that traditionally requires collaboration between SEO experts, content strategists, and front-end developers into an automated process executable by a single person: website AI discoverability audit, content structure optimization, keyword research, a machine-parsable layer for data visualization. The auditor doesn't output suggestions; instead, it automatically detects the front-end framework and generates code fixes that can be directly submitted as a Pull Request. Around the same direction, he also built a supporting GEO analysis tool that can simultaneously send queries to ChatGPT, Claude, Gemini, and Perplexity to analyze brand mention rates, sentiment, market share, and competitive positioning, outputting interactive HTML reports and structured data.
The actual results demonstrate the product value of this toolset. Companies like Articuler AI and Axis Robotics used GTM Engineer Skills to complete the full process from research to Resource Center setup in a few hours, whereas such work traditionally requires dozens of hours of cross-team collaboration. This efficiency gap is not achieved by model capability, but by Perseus's deep understanding and productized breakdown of the GTM workflow: he broke down a vague "improve AI discoverability" requirement into standardized stages executable step-by-step by an agent, each with clear inputs, outputs, and quality checks. This toolchain is currently adopted by over a dozen startups and several Fortune 500 companies. The open-source tool is the entry point, the commercial product is the scaled extension, and both share the same technical core.
The project itself is valuable, but Perseus believes the proposition is more important: the capability boundary of the Skill system extends far beyond the engineering field. Product strategy, go-to-market, business analysis—any expertise that can be structurally described can be encoded into agent capabilities.
Second Judgment: AI Agent's Operational Boundary Should Not Stop at Browsers and APIs
The agent discussion in 2026 is dominated by browser agents and API integrations. LangGraph, CrewAI, and Google ADK constitute a thriving multi-agent orchestration ecosystem. But Perseus noticed a structural blind spot: most global digital activity happens in native mobile apps—social, payment, gaming, communication—and these apps lack public APIs and browser equivalents. Existing frameworks cannot operate WeChat, Douyin, WhatsApp, or Alipay. Mobile is the world's dominant computing interface, but the infrastructure for native mobile agents is almost zero.
Perseus's thinking is: Why is everyone teaching AI to operate browsers, but no one is seriously teaching it to operate phones? The prosperity of browser agents is largely because the web is naturally automation-friendly, with DOM, APIs, and mature toolchains like Playwright. But the phone is a completely different world. Native apps are black boxes, without structured interface descriptions; operations can only be performed by simulating human touches and swipes. The difficulty of this problem lies not in getting the LLM to understand whether a button should be pressed, but in building the entire execution layer infrastructure from scratch: device connection management, screen state parsing, device mutex between multiple agents, security boundaries for sensitive operations.
This judgment drove the birth of OpenPocket. It is an open-source framework that uses ADB to allow LLM-driven agents to autonomously operate Android devices, currently with about a dozen contributors and over 500 commits. What users are really doing with it speaks volumes: automatically managing social media accounts, replying to messages in IMs for you, handling payments and bills on the phone, even automatically playing mobile games. A typical scenario is: the user tells the agent in natural language "Open Slack every morning at 8 am to check in," and the agent will persistently run this task in an isolated session, turning a previously manual, repetitive daily operation into background automation.
Perseus made several key product and architectural choices in this project. First, agents can automatically create new Skills during runtime. When encountering an unfamiliar operation flow, it can save the learned steps as a reusable SKILL.md for direct调用 next time. This means the agent is not a tool with fixed capabilities, but a system that grows stronger with use. Second, all sensitive operations must be approved by a human, rather than letting the agent judge what is safe. In his view, the most dangerous thing about autonomous agents is not that they do the wrong thing, but that they do the wrong thing "confidently" while thinking they are right. Third, each agent is completely isolated, bound to an independent device, configuration, and session state, allowing multiple agents to run simultaneously without interfering with each other. If only TypeScript engineers can extend the agent's capabilities, this ecosystem will never grow large, so OpenPocket, like Claude Code, uses SKILL.md as the standard format for capability extension.
The entire system supports 29+ LLM configurations. Agent phones are completely isolated from users' personal phones, and all data remains local. In 2026, with OWASP listing "Tool Misuse" among the Top 10 Risks for Agentic AI and the high-risk obligations of the EU AI Act about to take effect, this local-first, human-in-the-loop design is not conservative but a prerequisite for agents entering real-world scenarios.
Third Judgment: The Value of Open Source Lies Not in the Code Itself, But in the Definition of Standards at the Infrastructure Layer
Perseus's understanding of open source is not "putting code on GitHub." He repeatedly mentions a viewpoint: The open-source AI ecosystem in 2026 is in a window where standards have not yet solidified. The architectural patterns and interface specifications adopted by the community now will become the industry's default infrastructure in the coming years. In this window, defining a niche is more important than optimizing an existing solution.
Specifically, his Skill project pushed forward something technically meaningful: proving that the SKILL.md format is not just a container for engineering tools, but a sufficiently general standard for encoding domain knowledge. When the same SKILL.md can be loaded and executed by Claude Code, OpenAI Codex CLI, and OpenClaw, it de facto becomes the "portable capability unit" of the AI agent ecosystem. Perseus stuffed the complete workflow of go-to-market—a non-engineering field—into this format and successfully ran end-to-end automation from audit to code fix. This is a significant validation of the generality of the entire Skill standard.
His mobile agent project addresses an architectural gap at the agent execution layer. Existing agent frameworks rely on structured interfaces at the tool-calling level, either APIs or DOM. OpenPocket must operate in an environment without any structured interface, relying purely on screen pixel parsing and touch event injection. This forced the project to redesign the agent's perception-decision-execution loop from the ground up, including real-time parsing of device state, device mutex protocols for multiple agents, and automatic recovery mechanisms after operation failures. These are not simple adaptations of existing agent frameworks, but an architectural solution independently evolved for the problem of "autonomous operation in API-less environments."
The engineering design of the two projects is worth mentioning separately. OpenPocket adopts a three-layer separated architecture of Manager, Gateway, and Agent Runtime, where each layer can be iterated independently, and community contributors only need to focus on the layer they are familiar with. Each Skill within GTM Engineer Skills follows a staged pipeline design internally, where the output of the previous stage is the input of the next, with mandatory quality check gates in between. The workflow can be interrupted and resumed at any stage, and errors can be pinpointed to a specific stage. The purpose of these architectural choices is the same: to make the open-source project trustworthy for real users in production environments.
From a product perspective, these two projects also share a commonality: Perseus always places "who will use it" and "how to extend it" at the forefront of architectural decisions. The target users of GTM Engineer Skills are not engineers but growth teams, so each Skill has clear input-output contracts and built-in quality checks, allowing non-technical users to understand what the agent is doing. OpenPocket's SKILL.md extension mechanism, natural language scheduled tasks, and multi-channel access (Telegram, Discord, WhatsApp, CLI) are all designed to lower the barrier to entry for non-engineering users. In his view, if an open-source infrastructure project can only be used by engineers, its ceiling is the size of the engineering community. The truly leveraged design is to enable the boundary of agent capabilities to be expanded collectively by practitioners from all fields.
This pattern runs through his multiple projects. It's not about doing application-layer development on existing frameworks, but identifying missing components in the infrastructure layer of the agent ecosystem and then building them.
The Bigger Picture
The open-source AI ecosystem in 2026 is experiencing a moment similar to the early cloud-native ecosystem of the 2010s: standards and tools at the infrastructure layer are being defined, and these definitions will constrain the entire industry's development path for years to come. In this window, every Skill format adopted by the community, every agent architectural pattern validated, every ecosystem gap filled, is participating in shaping the next interface layer of AI.
What Perseus Yang is doing is simple: using engineering capability and product thinking to explore the paradigm at the technological frontier of the AI era. Models will continue to become more powerful, but who defines how agents should interact with the real world, who decides in what form domain knowledge should be encoded and distributed—the answers to these questions will not grow out of models. They can only be figured out bit by bit by people who build things.
