Wrap the agent loop in a runtime-guidance DSL and skill success rises 24.1% — tested on Claude Code and Codex directly
SkillSentry (arXiv 2608.09253, Aug 10) addresses the gap where an agent can complete a task under skill guidance but fails the same task on a repeat run. It defines a domain-specific language for runtime guidance, initializes it from skill specs extracted from documentation plus insights mined from historical successful *and* failed execution traces, wraps the agent execution loop to monitor and steer in real time, then iteratively refines the guidance from newly collected traces. Across 15 skills evaluated on Claude Code (Haiku 4.5, Opus 4.6) and Codex (GPT-5.2, GPT-5.4) it lifted task success 24.1% on average and — the underrated result — cut variance across repeated executions, which is what makes a skill trustworthy in an unattended pipeline.
↳ Follow the thread