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About this paper
Mohammad Zare, Omid Abdolrahmani
The MicroSkill Architecture proposes a framework for AI-native code generation that partitions system knowledge into discrete "skill capsules" and uses a dynamic router to provide task-relevant context to LLMs. This approach reduces token consumption by 93.4%, increases first-attempt compilation success to 86.6%, eliminates architectural violations, and enables autonomous skill acquisition.
Key insights
- Modularizing system knowledge into sharply scoped "skill capsules" effectively mitigates the "Lost in the Middle" phenomenon and reduces context window size for LLMs.
- Dynamic, budget-constrained routing of semantically relevant skills improves code generation efficiency, accuracy, and overall development sustainability.
- Embedding machine-enforceable architectural guardrails directly into skill capsules prevents architectural drift and ensures long-term structural integrity of AI-generated code.
Summary by alphaXiv.
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