Add Copilot skill architecture with 10 skills, 2 agents, and shared infra

Introduce .github/skills/ with solve, prove, optimize, simplify,
encode, explain, benchmark, memory-safety, static-analysis, and
deeptest skills. Each skill follows a SKILL.md + scripts/ pattern
with Python scripts backed by a shared SQLite logging library
(z3db.py). Two orchestrator agents (z3-solver, z3-verifier) route
requests to the appropriate skills.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

.github/skills/optimize/SKILL.md

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+---
+name: optimize
+description: Solve constrained optimization problems using Z3. Supports minimization and maximization of objective functions over integer, real, and bitvector domains.
+---
+
+Given a set of constraints and an objective function, find the optimal value. Z3 supports both hard constraints (must hold) and soft constraints (weighted preferences), as well as lexicographic multi-objective optimization.
+
+# Step 1: Formulate the problem
+
+The formula uses the `(minimize ...)` or `(maximize ...)` directives followed by `(check-sat)` and `(get-model)`.
+
+Example: minimize `x + y` subject to `x >= 1`, `y >= 2`, `x + y <= 10`:
+```smtlib
+(declare-const x Int)
+(declare-const y Int)
+(assert (>= x 1))
+(assert (>= y 2))
+(assert (<= (+ x y) 10))
+(minimize (+ x y))
+(check-sat)
+(get-model)
+```
+
+# Step 2: Run the optimizer
+
+```bash
+python3 scripts/optimize.py --file scheduling.smt2
+python3 scripts/optimize.py --formula "<inline smt-lib2>" --debug
+```
+
+# Step 3: Interpret the output
+
+- `sat` with a model: the optimal assignment satisfying all constraints.
+- `unsat`: the constraints are contradictory; no feasible solution exists.
+- `unknown` or `timeout`: Z3 could not determine optimality.
+
+The script prints the objective value and the satisfying assignment.
+
+# Parameters
+
+| Parameter | Type | Required | Default | Description |
+|-----------|------|----------|---------|-------------|
+| formula | string | no | | SMT-LIB2 formula with minimize/maximize |
+| file | path | no | | path to .smt2 file |
+| timeout | int | no | 60 | seconds |
+| z3 | path | no | auto | path to z3 binary |
+| debug | flag | no | off | verbose tracing |
+| db | path | no | .z3-agent/z3agent.db | logging database |

.github/skills/optimize/scripts/optimize.py

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+#!/usr/bin/env python3
+"""
+optimize.py: solve constrained optimization problems via Z3.
+
+Usage:
+    python optimize.py --file scheduling.smt2
+    python optimize.py --formula "(declare-const x Int)..." --debug
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent / "shared"))
+from z3db import Z3DB, run_z3, parse_model, setup_logging
+
+
+def main():
+    parser = argparse.ArgumentParser(prog="optimize")
+    parser.add_argument("--formula")
+    parser.add_argument("--file")
+    parser.add_argument("--timeout", type=int, default=60)
+    parser.add_argument("--z3", default=None)
+    parser.add_argument("--db", default=None)
+    parser.add_argument("--debug", action="store_true")
+    args = parser.parse_args()
+
+    setup_logging(args.debug)
+
+    if args.file:
+        formula = Path(args.file).read_text()
+    elif args.formula:
+        formula = args.formula
+    else:
+        parser.error("provide --formula or --file")
+        return
+
+    db = Z3DB(args.db)
+    run_id = db.start_run("optimize", formula)
+
+    result = run_z3(formula, z3_bin=args.z3, timeout=args.timeout, debug=args.debug)
+
+    model = parse_model(result["stdout"]) if result["result"] == "sat" else None
+
+    db.log_formula(run_id, formula, result["result"],
+                   str(model) if model else None)
+    db.finish_run(run_id, result["result"], result["duration_ms"],
+                  result["exit_code"])
+
+    print(result["result"])
+    if model:
+        for name, val in model.items():
+            print(f"  {name} = {val}")
+
+    db.close()
+    sys.exit(0 if result["exit_code"] == 0 else 1)
+
+
+if __name__ == "__main__":
+    main()