What is Hazelnut and why it matters
OpenAI is reportedly testing a new feature that could shift ChatGPT’s customization away from the familiar Custom GPTs toward a more modular system focused on reusable Skills. Code-named Hazelnut internally, the project aims to let both users and developers teach the AI model new capabilities in a more granular, plug-and-play fashion. If realized at scale, Hazelnut could redefine how people interact with chat-based AI by reducing setup time, increasing reuse of capabilities, and enabling safer, more auditable behavior changes.
Traditional Custom GPTs let users tailor a model to execute specific tasks or follow particular rules. Hazelnut appears to take this concept further by decomposing competencies into discrete Skills that can be combined as needed. Think of Skills as building blocks—each encapsulating a defined behavior, constraint, or data interaction—that can be mixed, matched, and extended without rewriting large prompts or retraining the model.
The technical promise of a modular Skills system
Several industry observers note that modular Skills could offer several advantages over monolithic instruction sets. A Skills approach could enable faster deployment of new capabilities, easier auditing of decisions, and improved safety controls because each Skill can be inspected and updated independently. Developers might publish a catalog of Skills—ranging from data processing routines to domain-specific reasoning patterns—that users can assemble into bespoke AI workflows. The modularity also opens possibilities for cross-application compatibility, where a Skill created for one task can be repurposed for another with minimal adaptation.
Potential benefits for users and developers
For end users, Hazelnut could dramatically reduce the friction of customizing an AI assistant. Rather than crafting a long prompt or juggling multiple Custom GPTs, a user could select a set ofSkills that align with their goals, configure lightweight constraints, and fine-tune behavior through a UI that mirrors app modularity. This could make advanced customization more accessible to non-technical users while offering power users a deeper level of control.
For developers, a Skill marketplace or library could become a new frontier for AI tooling. Third-party creators might publish domain-specific Skills—such as legal drafting checks, medical data interpretation (within strict safety boundaries), or financial anomaly detection—that can be integrated into different ChatGPT instances. The modular approach could also streamline updates. Instead of pushing an entire model revision, teams could iterate on individual Skills, improving performance or safety without disrupting other capabilities.
<h2Safety, privacy, and governance considerations
Following the rapid expansion of AI customization features, governance remains a central concern. A modular Skills framework would need robust safety rails: clear boundaries around data handling, rigorous auditing of Skill behaviors, and reliable isolation to prevent cross-Skill contamination of outputs. Privacy protections must be baked in, especially if Skills process sensitive information or access external data sources. OpenAI’s ongoing work with guardrails, red-teaming, and policy enforcement is likely to accompany any rollout of Hazelnut-like capabilities.
<h2 What comes next for OpenAI and the broader AI landscape
Hazelnut signals a broader industry shift toward composable AI capabilities. If OpenAI successfully demonstrates a scalable, safe, and user-friendly Skills system, other players may follow with their own modular frameworks, potentially accelerating innovation and competition. For users, the trend promises more personalized AI experiences without sacrificing accountability. For the market, it could open pathways for AI as a service where businesses tailor a suite of Skills to fit their workflows with greater ease than ever before.
As with any early-stage feature, details remain speculative until OpenAI confirms product plans, rollout timelines, and governance models. Still, the Hazelnut concept highlights a clear trajectory: moving beyond static customization toward a flexible, modular, and auditable set of AI capabilities that empower both creators and users to shape intelligent systems with precision.
