Learning Globally Reusable Skills for Coding Agents

TL;DR - GSE is a skill-evolution framework for LLM coding agents that treats a skill bank as a globally coupled system rather than a series of local edits, yielding skills that generalize across software-engineering tasks. It matters because it offers continual agent improvement without retraining, with reported gains on both open agents and an internal industrial deployment.

• Maintains a Skill Relation Graph (SRG) that explicitly models and co-evolves inter-skill relationships to keep the skill bank consistent, jointly optimizing skill compatibility and generalization.
• Uses cluster-based skill consolidation to abstract reusable capabilities from local updates, plus replay-driven verification to guard against overfitting and behavioral regressions.
• Evaluated on bug-revealing test generation and false-positive bug report filtering with OpenHands and mini-SWE-agent; best precision/recall/F1 in all cases.
• Reported gains over prior evolution techniques: +6.1%~34.1% precision and +31.8%~180.0% recall (test generation), +15.4%~96.4% precision and +13.1%~19.8% recall (FP filtering); +61.4% F1 on an internal industrial agent.