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SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?

arXiv:2603.1540195.620 citationsh-index: 16Has Code
Predicted impact top 4% in SE · last 90 daysOriginality Incremental advance
AI Analysis

This work addresses the problem of evaluating agent skills for software engineering practitioners, revealing their limited and context-dependent benefits, which is incremental as it provides a new benchmark for testing.

The paper tackles the unclear real utility of agent skills in software engineering by introducing SWE-Skills-Bench, a benchmark that isolates their marginal impact, finding that most skills yield zero improvement with an average gain of only +1.2%, though a few specialized ones show up to +30% gains.

Agent skills, structured procedural knowledge packages injected at inference time, are increasingly used to augment LLM agents on software engineering tasks. However, their real utility in end-to-end development settings remains unclear. We present SWE-Skills-Bench, the first requirement-driven benchmark that isolates the marginal utility of agent skills in real-world software engineering (SWE). It pairs 49 public SWE skills with authentic GitHub repositories pinned at fixed commits and requirement documents with explicit acceptance criteria, yielding approximately 565 task instances across six SWE subdomains. We introduce a deterministic verification framework that maps each task's acceptance criteria to execution-based tests, enabling controlled paired evaluation with and without the skill. Our results show that skill injection benefits are far more limited than rapid adoption suggests: 39 of 49 skills yield zero pass-rate improvement, and the average gain is only +1.2%. Token overhead varies from modest savings to a 451% increase while pass rates remain unchanged. Only seven specialized skills produce meaningful gains (up to +30%), while three degrade performance (up to -10%) due to version-mismatched guidance conflicting with project context. These findings suggest that agent skills are a narrow intervention whose utility depends strongly on domain fit, abstraction level, and contextual compatibility. SWE-Skills-Bench provides a testbed for evaluating the design, selection, and deployment of skills in software engineering agents. SWE-Skills-Bench is available at https://github.com/GeniusHTX/SWE-Skills-Bench.

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