Agent skill

root-finding

Problem-solving strategies for root finding in numerical methods

parcadei3,879★ · 1 repos on radarProfile →
claude-codecan modify filesMIT
Install
npx skills add parcadei/Continuous-Claude-v3 --skill root-finding --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 3 KB
Bundled scripts: none
Allowed tools: BashRead
Path: .claude/skills/math/numerical-methods/root-finding/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 3,885
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Root Finding ## When to Use Use this skill when working on root-finding problems in numerical methods. ## Decision Tree 1. **Characterize the Problem** - Single root or multiple roots? - Bracketed (know interval containing root)? - Derivatives available? 2. **Method Selection** | 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 | 3. **Implement Root Finding** - `scipy.optimize.brentq(f, a, b)` - guaranteed convergence if bracketed - `scipy.optimize.newton(f, x0, fprime=df)` - quadratic convergence near root - For systems: `scipy.optimize.fsolve(F, x0)` 4. **Handle Multiple Roots** - Deflation: divide out found roots - Multiple starting points - `sympy_compute.py solve "f(x)" --var x` for symbolic solutions 5. **Verify Solutions** - Check |f(root)| < tolerance - Verify root is in expected domain - `z3_solve.py prove "f(root) == 0"` ## Tool Commands ### Scipy_Brentq

What's inside
Steps it walks through
  1. When to Use
  2. Decision Tree
  3. Tool Commands
  4. ScipyBrentq
  5. ScipyNewton
  6. SympySolve
  7. Key Techniques
  8. Cognitive Tools Reference
Commands it runs
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 x
More from Continuous-Claude-v3
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About this skill
What does the root-finding skill do?

Problem-solving strategies for root finding in numerical methods

How do I install it?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill root-finding --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From parcadei/Continuous-Claude-v3, a repository with 3,885 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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