Overview: A new modular path for ChatGPT
In a step that could redefine how people customize and extend ChatGPT, OpenAI is exploring a shift from its well-known Custom GPTs toward a modular system built around what is being called Skills. Codenamed Hazelnut internally, the project reportedly aims to let both users and developers teach the AI model new capabilities in a more granular, plug-and-play fashion. If realized, this approach could transform how businesses deploy AI, how creators tailor assistants, and how end users interact with chat-based tools that learn and adapt over time.
What are “Skills” and how do they differ from Custom GPTs?
Custom GPTs currently let users tailor a GPT instance with specific rules, data, and behaviors. The proposed Skills framework would take a more modular route: discrete, well-defined capabilities that can be composed, re-used, and shared across applications. Think of Skills as building blocks for AI behavior—each block encapsulating a task, a domain knowledge slice, or a procedural capability. Rather than rebuilding a large GPT for every use case, developers could assemble and tweak a portfolio of Skills to meet particular needs.
How Hazelnut could work in practice
Details remain speculative, but the concept centers on codifying AI competencies as standalone modules. A developer might publish a Skill that handles contract clause extraction, another that supports multilingual customer support, and another that reasons about financial risk summaries. These Skills could be added to a user’s chat environment or integrated into enterprise workflows with standard interfaces and safety controls. The model would orchestrate these Skills, selecting the right module based on context and user intent, much like an app ecosystem but at the AI capability level.
Potential benefits for users and developers
Several advantages stand out if the modular Skills approach gains traction. For users, this could mean faster tailoring of AI assistants to specific tasks without writing custom prompts or sharing sensitive data broadly. For developers and businesses, Skills promises greater reusability, easier updates, and a path to certifications or governance around capabilities. By clearly delineating responsibilities, OpenAI could also improve safety and compliance, since each Skill would be subject to its own testing and controls.
Security, privacy, and governance considerations
Modular AI introduces new questions around how Skills are isolated, updated, and audited. Enterprises will want robust sandboxing, version control, and provenance data showing which Skills contributed to a given answer. Privacy implications include how data flows through Skills and whether any module retains information beyond the immediate session. OpenAI and partner developers are likely to emphasize transparent usage policies, sandboxed execution, and opt-in sharing of Skills across environments to mitigate risk.
Impact on the AI ecosystem
Should Hazelnut and similar modular approaches succeed, the landscape could tilt toward an ecosystem of AI “capabilities” that can be mixed and matched. For users, this means greater control over tool behavior without heavy custom development. For developers, it creates opportunities to monetize and curate high-quality Skills while partnering with OpenAI to ensure interoperability. The shift could spur new standards around Skill interfaces, testing, and safety metrics, accelerating collaboration across platforms and industries.
What this signals for the near term
While official timelines remain uncertain, the Hazelnut project underscores OpenAI’s interest in evolving how AI is extended and governed. The move toward modular Skills aligns with broader industry trends toward composable AI, where capabilities are independently developed, verified, and deployed. For now, observers should watch for announcements about public tooling, documentation on Skill interfaces, and early pilots in sectors like customer service, content moderation, and data analysis.
Conclusion: A potential turning point in AI customization
If OpenAI brings Skills to a broad audience, users could experience a more flexible, safer, and scalable way to tailor ChatGPT to real-world tasks. The transition from Custom GPTs to a Skills-based model represents not just a product tweak but a shift in how AI capabilities are designed, shared, and governed. As with any evolving technology, success will hinge on clear standards, strong safeguards, and meaningful collaboration between developers, enterprises, and end users.
