SkillEffect: Checked-Lowering Runtime for Memory-Bounded AI Agent Tools
SkillEffect acts as a runtime system that guarantees verified lowering for tasks needing a safe source relation, a validated bounded implementation, and a clear output postcondition. It tackles the issue with language models that convert Agent Skills into specific programs. Although semantically correct, these models can strain memory by loading entire inputs at once. Execution only occurs after a separate verifier reconstructs each proposed lowering using the original program and unchanged input. Each plugin includes components like a source recognizer, an input-fact extractor, a bounded-IR constructor, an arena-bound function, and a postcondition. The paper highlights SkillEffect's architectural flexibility and focuses on safe resource usage in AI tools, published on arXiv as entry 2608.17007.
Key facts
- SkillEffect is a checked-lowering runtime for memory-bounded agent tools.
- It addresses the problem of language-model-generated code exceeding tool call memory limits.
- An independent checker rebuilds each proposed lowering from the submitted program and immutable input before execution.
- Each relation plugin supplies a source recognizer, input-fact extractor, bounded-IR constructor, arena-bound function, and postcondition.
- The common runtime provides checked selection, bounded-VM execution, atomic capacity leasing, and staged publication.
- Generality in SkillEffect is architectural.
- The paper is available on arXiv under identifier 2608.17007.
- Agent Skills specify procedural and resource obligations that language models instantiate as programs.
Entities
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