Given a problem description (natural language, pseudocode, or a partial formulation), produce a complete, syntactically valid SMT-LIB2 encoding or Z3 Python script. The encoding should declare all variables, assert all constraints, and include the appropriate check-sat / get-model commands.

Step 1: Identify the problem class

Action: Determine the SMT theory and variable sorts required by the problem description.

Expectation: A clear mapping from the problem to one of the supported theories (LIA, LRA, QF_BV, etc.).

Result: If the theory is identified, proceed to Step 2. If the problem spans multiple theories, select the appropriate combined logic.

Step 2: Generate the encoding

Action: Invoke encode.py with the problem description and desired output format.

Expectation: The script produces a complete SMT-LIB2 file or Z3 Python script with all declarations, constraints, and check-sat commands.

Result: For smtlib2 format: pass the output to solve. For python format: execute the script directly. Proceed to Step 3 for validation.

python3 scripts/encode.py --problem "Find integers x, y such that x^2 + y^2 = 25 and x > 0" --format smtlib2
python3 scripts/encode.py --problem "Schedule 4 tasks on 2 machines minimizing makespan" --format python

Step 3: Validate the encoding

Action: The script runs a syntax check by piping the output through z3 -in in parse-only mode.

Expectation: No parse errors. If errors occur, the offending line is reported.

Result: On success: the encoding is ready for solve, prove, or optimize. On parse error: fix the reported line and re-run.

Parameters