Given raw Z3 output (from the solve, prove, optimize, or benchmark skills), produce a structured explanation. This skill is for cases where the solver output is large, nested, or otherwise difficult to read directly.

Step 1: Identify the output type

Action: Determine the category of Z3 output to explain: model, core, statistics, error, or proof.

Expectation: The output type maps to one of the recognized formats in the table below.

Result: If the type is ambiguous, use --type auto and let the script detect it. Proceed to Step 2.

Step 2: Run the explainer

Action: Invoke explain.py with the output file or stdin.

Expectation: The script auto-detects the output type and produces a structured plain-language summary.

Result: A formatted explanation is printed. If detection fails, re-run with an explicit --type flag.

python3 scripts/explain.py --file output.txt
python3 scripts/explain.py --stdin < output.txt
python3 scripts/explain.py --file output.txt --debug

Step 3: Interpret the explanation

Action: Review the structured explanation for accuracy and completeness.

Expectation: Models list each variable with its value and sort. Cores list conflicting assertions. Statistics show time and memory breakdowns.

Result: Use the explanation to answer the user query or to guide the next skill invocation.

For models:

• Each variable is listed with its value and sort
• Array and function interpretations are expanded
• Bitvector values are shown in decimal and hex

For unsat cores:

• The conflicting named assertions are listed
• A minimal conflict set is highlighted

For statistics:

• Time breakdown by phase (preprocessing, solving, model construction)
• Theory solver load distribution
• Memory high-water mark

Parameters