SKILLFOUNDRY: Building Self-Evolving Agent Skill Libraries from Heterogeneous Scientific Resources
arXiv:2604.03964v1 Announce Type: new Abstract: Modern scientific ecosystems are rich in procedural knowledge across repositories, APIs, scripts, notebooks, documentation, databases, and papers, yet much of this knowledge remains fragmented across heterogeneous artifacts that agents cannot readily operationalize. This gap between abundant scientific know-how and usable agent capabilities is a key bottleneck for building effective scientific agents. We present SkillFoundry, a self-evolving framework that converts such resources into validated agent skills, reusable packages that encode task scope, inputs and outputs, execution steps, environment assumptions, provenance, and tests. SkillFoundry organizes a target domain as a domain knowledge tree, mines resources from high-value branches, extracts operational contracts, compiles them into executable skill packages, and then iteratively expands, repairs, merges, or prunes the resulting library through a closed-loop validation process. Sk
arXiv:2604.03964v1 Announce Type: new Abstract: Modern scientific ecosystems are rich in procedural knowledge across repositories, APIs, scripts, notebooks, documentation, databases, and papers, yet much of this knowledge remains fragmented across heterogeneous artifacts that agents cannot readily operationalize. This gap between abundant scientific know-how and usable agent capabilities is a key bottleneck for building effective scientific agents. We present SkillFoundry, a self-evolving framework that converts such resources into validated agent skills, reusable packages that encode task scope, inputs and outputs, execution steps, environment assumptions, provenance, and tests. SkillFoundry organizes a target domain as a domain knowledge tree, mines resources from high-value branches, extracts operational contracts, compiles them into executable skill packages, and then iteratively expands, repairs, merges, or prunes the resulting library through a closed-loop validation process. SkillFoundry produces a substantially novel and internally valid skill library, with 71.1\% of mined skills differing from existing skill libraries such as SkillHub and SkillSMP. We demonstrate that these mined skills improve coding agent performance on five of the six MoSciBench datasets. We further show that SkillFoundry can design new task-specific skills on demand for concrete scientific objectives, and that the resulting skills substantially improve performance on two challenging genomics tasks: cell type annotation and the scDRS workflow. Together, these results show that automatically mined skills improve agent performance on benchmarks and domain-specific tasks, expand coverage beyond hand-crafted skill libraries, and provide a practical foundation for more capable scientific agents.
Executive Summary
SkillFoundry addresses a critical bottleneck in the operationalization of scientific procedural knowledge by introducing a self-evolving framework that transforms heterogeneous scientific resources into validated, reusable agent skills. The framework constructs a domain knowledge tree, mines high-value resources, extracts operational contracts, and compiles these into executable skill packages. Through a closed-loop validation process, SkillFoundry iteratively refines its skill library, achieving 71.1% novelty compared to existing libraries like SkillHub and SkillSMP. Empirical validation demonstrates improvements in coding agent performance across five of six MoSciBench datasets and significant gains in domain-specific genomics tasks such as cell type annotation and the scDRS workflow. The system’s ability to autonomously design task-specific skills further underscores its potential to enhance the capabilities of scientific agents.
Key Points
- ▸ SkillFoundry bridges the gap between fragmented scientific procedural knowledge and operationalizable agent skills by converting heterogeneous resources into validated, reusable skill packages.
- ▸ The framework employs a domain knowledge tree and closed-loop validation to iteratively expand, repair, merge, or prune its skill library, ensuring internal validity and novelty (71.1% difference from existing libraries).
- ▸ Empirical results show that mined skills improve agent performance on MoSciBench datasets and domain-specific genomics tasks, highlighting the framework’s practical utility and adaptability.
Merits
Innovative Framework Design
SkillFoundry’s integration of domain knowledge trees, operational contract extraction, and closed-loop validation represents a novel approach to automating the conversion of heterogeneous scientific knowledge into executable skills, addressing a longstanding challenge in agent-based systems.
Empirical Validation and Performance Gains
The framework demonstrates tangible improvements in agent performance across multiple benchmarks and domain-specific tasks, providing robust evidence of its efficacy and scalability.
Autonomy and Adaptability
SkillFoundry’s ability to design task-specific skills on demand and iteratively refine its skill library underscores its potential for continuous evolution, reducing the need for manual intervention and enhancing long-term utility.
Demerits
Dependency on High-Quality Input Resources
The efficacy of SkillFoundry is contingent on the quality and accessibility of the scientific resources it mines. Poorly documented or fragmented resources may limit the framework’s ability to extract meaningful operational contracts.
Computational and Resource Overhead
The iterative closed-loop validation process and domain knowledge tree construction may impose significant computational and resource demands, particularly for large-scale or highly complex domains.
Limited Generalizability to Non-Scientific Domains
While SkillFoundry excels in scientific ecosystems, its applicability to non-scientific domains or less structured knowledge environments remains untested, potentially limiting its broader impact.
Expert Commentary
SkillFoundry represents a significant leap forward in bridging the gap between theoretical scientific knowledge and practical agent capabilities. By systematically converting heterogeneous resources into validated, reusable skills, the framework not only enhances the performance of scientific agents but also paves the way for more autonomous and adaptive AI systems. The closed-loop validation process is particularly noteworthy, as it ensures continuous refinement and improvement of the skill library, addressing a critical challenge in dynamic environments. However, the framework’s reliance on high-quality input resources and its computational demands may pose barriers to adoption in resource-constrained settings. Furthermore, the potential for unintended consequences in high-stakes applications necessitates robust governance frameworks. Overall, SkillFoundry is a pioneering effort that merits further exploration, particularly in terms of scalability, interoperability, and ethical considerations.
Recommendations
- ✓ For organizations seeking to adopt SkillFoundry, it is advisable to invest in high-quality documentation and standardization of scientific resources to maximize the framework’s efficacy and reduce the risk of poor skill extraction.
- ✓ Policymakers and industry leaders should collaborate to develop interoperability standards for scientific APIs and scripts, ensuring seamless integration with autonomous agent systems and fostering a more collaborative and efficient scientific ecosystem.
- ✓ Future research should explore the framework’s applicability to non-scientific domains and assess its scalability in low-resource environments to broaden its impact and accessibility.
Sources
Original: arXiv - cs.AI
