z3/.github/skills

Z3 Agent Skills

Reusable, composable verification primitives for the Z3 theorem prover. Each skill is a self-contained unit: a SKILL.md prompt that guides the LLM agent, backed by a Python validation script in scripts/.

Skill Catalogue

Skill	Status	Description
solve	implemented	Check satisfiability of SMT-LIB2 formulas; return models or unsat cores
prove	implemented	Prove validity by negation and satisfiability checking
encode	implemented	Translate constraint problems into SMT-LIB2 or Z3 Python API code
simplify	implemented	Reduce formula complexity using configurable Z3 tactic chains
optimize	implemented	Solve constrained optimization (minimize/maximize) over numeric domains
explain	implemented	Parse and interpret Z3 output: models, cores, statistics, errors
benchmark	implemented	Measure Z3 performance and collect solver statistics
static-analysis	implemented	Run Clang Static Analyzer on Z3 source and log structured findings
memory-safety	implemented	Run ASan/UBSan on Z3 test suite to detect memory errors and undefined behavior

Agent

A single orchestration agent composes these skills into end-to-end workflows:

Agent	Role
z3	SMT solving, code quality analysis, and stress testing

Shared Infrastructure

All scripts share a common library at shared/z3db.py with:

• Z3DB: SQLite wrapper for tracking runs, formulas, findings, and interaction logs.
• run_z3(): Pipe SMT-LIB2 into z3 -in with timeout handling.
• find_z3(): Locate the Z3 binary across build directories and PATH.
• Parsers: parse_model(), parse_stats(), parse_unsat_core().

The database schema lives in shared/schema.sql.

Relationship to a3/ Workflows

The a3/ directory at the repository root contains two existing agentic workflow prompts that predate the skill architecture:

• a3/a3-python.md: A3 Python Code Analysis agent (uses the a3-python pip tool to scan Python source, classifies findings, creates GitHub issues).
• a3/a3-rust.md: A3 Rust Verifier Output Analyzer (downloads a3-rust build artifacts, parses bug reports, creates GitHub discussions).

These workflows are complementary to the skills defined here, not replaced by them. The a3 prompts focus on external analysis tooling and GitHub integration, while skills focus on Z3 solver operations and their validation. Both may be composed by the same orchestrating agent.

Usage

Check database status and recent runs:

python shared/z3db.py status
python shared/z3db.py runs --skill solve --last 5
python shared/z3db.py log --run-id 12
python shared/z3db.py query "SELECT skill, COUNT(*) FROM runs GROUP BY skill"

Run an individual skill script directly:

python solve/scripts/solve.py --file problem.smt2
python encode/scripts/encode.py --validate formula.smt2
python benchmark/scripts/benchmark.py --file problem.smt2