Harness & skills

This talk addresses the significant security risks present in AI agent skills, highlighting that approximately one in eight skills contain critical vulnerabilities. These risks include the potential for malicious actors to exploit these weaknesses to gain unauthorized access to sensitive information, such as API keys. The presentation emphasizes the urgent need for robust security practices and thorough vetting of AI agent components.

This talk posits that AI skills are evolving into a new form of Software Development Kits (SDKs), fundamentally changing how developers build and interact with AI systems. The core thesis is that by abstracting complex AI functionalities into reusable skills, developers can accelerate innovation and create more sophisticated applications. This shift emphasizes modularity and interoperability, akin to traditional software libraries.

This talk emphasizes the critical need for rigorous evaluation of AI skills before deployment, arguing that shipping skills without proper testing can lead to unpredictable behavior and degraded performance. The speaker highlights the distinction between agents developers use for personal productivity and agents built for consumers, noting that end-users lack the context to troubleshoot skill invocation issues. The core thesis is that comprehensive evaluations are essential for ensuring skill reliability, managing costs, and determining when skills can be retired as models improve.

This talk addresses the growing problem of "skill hell" in AI development, where developers struggle to effectively use and integrate available skills. The core thesis is that a lack of clear criteria for what constitutes a "great skill" hinders progress. The presentation offers a structured checklist and practical techniques to help developers build, evaluate, and improve AI skills, moving beyond the current state of confusion and inefficiency.

This talk argues that as AI becomes proficient at generating code, the critical bottleneck in software development shifts from *how* to build to *what* to build. The ability to understand business needs, elicit requirements, and make strategic decisions becomes the most valuable skill, surpassing traditional coding proficiency. This emphasizes the enduring importance of human-centric skills in guiding AI development.

This talk explores how to improve AI agent performance by reducing complexity and focusing on verifiable outcomes. The speaker advocates for enforcing actions through structured pipelines rather than relying on agent instructions alone. By minimizing unnecessary agent skills and implementing robust verification mechanisms, developers can achieve better results and reduce errors. The core idea is to shift from trusting agents to demanding proof of their work.

This talk emphasizes that the most significant benefit of AI in large codebases is not code generation, but comprehension. The speaker shares personal experience using AI, particularly Claude, to navigate complex codebases at Sentry. The core argument is that AI acts as an invaluable teammate for understanding code, which is crucial for maintaining quality and avoiding technical debt, especially when working on projects that are critical to one's livelihood.

This talk explores the challenges and lessons learned in skilling up coding agents to effectively use tools like Langfuse. It highlights the evolution from complex workflows to more autonomous agents and emphasizes the role of formalized skills as shortcuts for reliability. The core thesis is that by leveraging tracing and structured skills, developers can significantly improve agent capabilities and streamline the integration of complex tools into AI applications.

This talk explores how to effectively guide AI agents when they interact with complex products like Superbase. It argues that while agents are capable of many tasks, they require specific guidance to handle novel situations, security protocols, and optimized workflows. The presentation introduces the concept of "skills" as a method to provide this guidance, demonstrating how they improve agent performance and safety, particularly when integrated with tool-calling capabilities.

This talk explores the burgeoning open-source AI agent ecosystem, emphasizing the accessibility and power of open models and tools. It highlights how advancements in Hugging Face's platform, including the Hub, Traces, Skills, and MCP tools, are democratizing the development and deployment of sophisticated AI agents. The core thesis is that open-source AI is rapidly catching up to and even surpassing closed-source alternatives, offering greater control, privacy, and customization for developers.

This workshop explores the concept and practical application of "skills" in AI agents, presenting them as discrete, composable units of work designed to enhance agentic workflows. The core thesis is that skills provide a structured way to encode essential context, instructions, and behaviors, preventing repetitive information input and ensuring consistent, predictable agent performance across various tasks and projects.

This talk explores how to improve AI agent performance through the use of skills, which are essentially structured folders containing instructions and files for running workflows or providing custom information. The speaker, Pedro Rodrigues from Supabase, details the process of creating, testing, and automating the evaluation of these skills, emphasizing their role in enhancing agent capabilities within products like Supabase. The core idea is to leverage skills for progressive disclosure, allowing agents to access information only when needed, thereby optimizing context window usage.

This talk details how Cursor refactored a complex feature, originally spanning 12,000 lines of code, into a concise 200-line skill. This transformation leveraged existing primitives like agent skills and sub-agents, primarily using markdown to redefine functionality. The refactoring aimed to reduce maintenance overhead and improve user experience for advanced features.

This talk proposes a shift from building standalone AI agents to developing modular "skills" that agents can utilize. The core argument is that while agents possess intelligence, they often lack the specific domain expertise required for real-world tasks. Skills, defined as organized collections of files containing procedural knowledge and tools, offer a more composable and expertise-driven approach to enhancing agent capabilities. This paradigm aims to make AI more accessible and adaptable by packaging expertise into shareable units.

This talk explores the evolving skillset required to effectively utilize coding agents, moving beyond early chatbot paradigms. It argues that the current era demands a new approach to agent interaction, focusing on empowering agents to perform tasks autonomously rather than micromanaging them. The core thesis is that mastering coding agents is a high-ceiling skill that will significantly enhance developer productivity, akin to learning a new programming language or editor.