MECHASKILL: Automatic Compilation and Evolution of Modular Skills for Text-to-SQL Agents

Strong Text-to-SQL workflows offer complementary expertise, but their capabilities remain tied to workflow-specific execution logic, making them difficult to understand, reuse, and improve. Combining these strengths requires preserving the conditions under which each capability works and assessing how capabilities affect one another. We introduce MECHASKILL, a framework that compiles workflow expertise into a modular skill for a single agent and supports module-level evolution. It recovers modules with compatible interfaces from code, documentation, and execution traces, preserving the instructions, tools, and dependencies needed for reuse. We use complementary question coverage to guide composition and evaluate the complete skill to select modules and determine when they should run. We trace failures to individual modules to guide targeted revisions, then evaluate their effect on the complete skill. We evaluate MECHASKILL alongside models, agents, full workflows, and distilled skills on BIRD-147, SPIDER2-135, and BIZ-64. In our primary evaluation, MECHASKILL achieves the highest execution accuracy among the compared systems while substantially reducing inference cost relative to the strongest workflow baseline on each dataset.