SkillAudit: From Fixed-Suite Benchmarking to Skill-Centered Assessment

SkillAudit introduces an automated framework for evaluating AI agent skills independently of fixed task benchmarks, addressing a critical gap in skill marketplaces. The research reveals that over 7% of real-world skill packages exhibit risky behavior, highlighting the need for systematic assessment tools as AI skill ecosystems expand.

The emergence of agent skills as modular extensions to large language models has created a rapidly growing marketplace, but evaluation methodologies have failed to keep pace with this expansion. SkillAudit addresses a fundamental problem: existing benchmarking approaches measure skills against predetermined task suites, which conflates a skill's actual contribution with the underlying model's capabilities and misses value when tasks diverge from the skill's intended scope. This architectural flaw becomes increasingly problematic as skill marketplaces mature and users require reliable quality signals.

The framework represents a shift toward skill-centric rather than task-centric evaluation. By automatically generating capability-aligned evaluation tasks directly from skill packages and executing them in isolated sandbox environments, SkillAudit decouples assessment from static benchmarks. The methodology combines baseline comparison principles for utility and efficiency metrics with a two-stage safety detection paradigm incorporating both static semantic analysis and dynamic runtime verification.

The empirical findings carry significant implications for the AI infrastructure layer. The discovery that over 7% of skills across 23 occupational categories exhibit risky status suggests the skill ecosystem currently lacks adequate quality controls. For developers building on agent platforms, this creates both liability exposure and market differentiation opportunities for rigorously audited skills. For AI infrastructure providers, it indicates demand for automated evaluation tooling as a competitive advantage.

As skill marketplaces mature toward production deployment, standardized assessment frameworks become critical infrastructure. SkillAudit's approach establishes a template for skill evaluation that could influence how marketplaces implement quality standards and how enterprises make deployment decisions. The prevalence of risky skills points toward inevitable marketplace friction until evaluation becomes commoditized.

• →Fixed task benchmarks inadequately assess AI agent skills due to backbone model conflation and scope mismatch
• →SkillAudit automatically generates capability-aligned evaluation tasks directly from skill packages to enable independent assessment
• →Over 7% of scanned real-world skill packages exhibit risky behavior across 23 occupational categories
• →Two-stage safety detection combining static analysis with runtime verification identifies previously undetected risk patterns
• →Skill marketplaces require standardized evaluation frameworks to establish quality signals as deployment increases