root-finding — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited root-finding (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use this skill when working on root-finding problems in numerical methods.
| Situation | Method | Implementation |
|---|---|---|
| Bracketed, no derivatives | Bisection, Brent | scipy.optimize.brentq |
| Derivatives available | Newton-Raphson | scipy.optimize.newton |
| No derivatives | Secant method | scipy.optimize.newton (no fprime) |
| System of equations | scipy.optimize.fsolve | Requires Jacobian ideally |
scipy.optimize.brentq(f, a, b) - guaranteed convergence if bracketedscipy.optimize.newton(f, x0, fprime=df) - quadratic convergence near rootscipy.optimize.fsolve(F, x0)sympy_compute.py solve "f(x)" --var x for symbolic solutionsz3_solve.py prove "f(root) == 0"uv run python -c "from scipy.optimize import brentq; root = brentq(lambda x: x**2 - 2, 0, 2); print('Root:', root)"uv run python -c "from scipy.optimize import newton; root = newton(lambda x: x**2 - 2, 1.0, fprime=lambda x: 2*x); print('Root:', root)"uv run python -m runtime.harness scripts/sympy_compute.py solve "x**3 - x - 1" --var xFrom indexed textbooks:
See .claude/skills/math-mode/SKILL.md for full tool documentation.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.