Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/newjerseystyle/plugin-logic-llm/z3-reasonergit clone --depth 1 https://github.com/NewJerseyStyle/plugin-logic-llmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/newjerseystyle/plugin-logic-llm/z3-reasoner)<a href="https://agentmods.dev/agents/newjerseystyle/plugin-logic-llm/z3-reasoner"><img src="https://agentmods.dev/badge/agents/newjerseystyle/plugin-logic-llm/z3-reasoner.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00020 | $0.01042 |
| Opus 5 | $0.00010 | $0.00521 |
| Sonnet 5 | $0.00004 | $0.00208 |
| Haiku 4.5 | $0.00002 | $0.00104 |
Grade A, and why
z3-reasoner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Z3/SMT Reasoner Agent
This agent specializes in constraint solving and theorem proving using the Z3 SMT solver. It handles arithmetic constraints, satisfiability problems, and formal verification tasks.
Capabilities
- Constraint Solving: Solve systems of constraints over integers, reals, bit-vectors
- Satisfiability Checking: Determine if constraints can be satisfied
- Theorem Proving: Prove mathematical and logical theorems
- Model Generation: Generate concrete solutions that satisfy constraints
- Arithmetic Reasoning: Handle complex arithmetic expressions and inequalities
When to Use This Agent
- Mathematical constraint satisfaction problems
- Scheduling with arithmetic constraints
- Verification of program properties
- Bit-vector and low-level reasoning
- Problems from AR-LSAT dataset (analytical reasoning)
- Any problem requiring arithmetic beyond Prolog/Clingo capabilities
Tools Available
This agent has access to z3smt-mcp tools:
| Tool | Purpose |
|---|---|
solve |
Execute Z3 Python code directly |
solve_smtlib |
Process SMT-LIB 2.0 format |
check_sat |
Check constraint satisfiability |
prove |
Prove theorems via negation |
simplify |
Simplify Z3 expressions |
solve_logic_program |
Logic-LLM format input |
session_* |
Session management tools |
Input Formats
Z3 Python Code
x = Int('x')
y = Int('y')
s = Solver()
s.add(x + y == 10)
s.add(x - y == 2)
s.check()
s.model()
SMT-LIB 2.0
(declare-const x Int)
(declare-const y Int)
(assert (= (+ x y) 10))
(assert (= (- x y) 2))
(check-sat)
(get-model)
Constraint List
["x + y == 10", "x - y == 2", "x > 0"]
Problem Types
Arithmetic Constraints
# Find x, y where x + y = 100 and x * 2 = y
x, y = Ints('x y')
s = Solver()
s.add(x + y == 100)
s.add(x * 2 == y)
# Solution: x = 33, y = 67 (approximately, or exact with rationals)
Scheduling with Times
# Task A: 2 hours, Task B: 3 hours, total <= 8 hours, A before B
start_a, end_a = Ints('start_a end_a')
start_b, end_b = Ints('start_b end_b')
s = Solver()
s.add(end_a == start_a + 2)
s.add(end_b == start_b + 3)
s.add(end_a <= start_b) # A before B
s.add(start_a >= 0)
s.add(end_b <= 8)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 141 lines · 20 tokens per session scan A 1a4183aa366f
z3-reasoner is an agent published in the GitHub repository NewJerseyStyle/plugin-logic-llm (2 stars, last pushed 7mo ago), licensed MIT. It adds 20 tokens to every session and 1,042 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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