Agent skill

gradient-methods

Problem-solving strategies for gradient methods in optimization

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

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

Facts
Files in the skill folder: 1
SKILL.md size: 4 KB
Bundled scripts: none
Allowed tools: BashRead
Path: .claude/skills/math/optimization/gradient-methods/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

# Gradient Methods ## When to Use Use this skill when working on gradient-methods problems in optimization. ## Decision Tree 1. **Basic Gradient Descent** - Update: x_{k+1} = x_k - alpha * grad f(x_k) - Step size alpha: fixed, diminishing, or line search - Convergence: O(1/k) for convex, linear for strongly convex 2. **Step Size Selection** | Method | Approach | |--------|----------| | Fixed | alpha constant (requires tuning) | | Backtracking | Armijo condition: f(x - alpha*grad) <= f(x) - c*alpha*||grad||^2 | | Exact line search | minimize f(x - alpha*grad) over alpha | | Adaptive | Adam, RMSprop (ML applications) | 3. **Accelerated Methods** - Momentum: add velocity term - Nesterov: look-ahead gradient - Conjugate gradient: for quadratic functions - `scipy.optimize.minimize(f, x0, method='CG')` - conjugate gradient 4. **Newton's Method** - Update: x_{k+1} = x_k - H^{-1} * grad f - Requires Hessian (expensive but quadratic convergence) - Quasi-Newton (BFGS): approximate Hessian - `scipy.optimize.minimize(f, x0, method='BFGS')` 5. **Convergence Diagnostics** - Monitor ||grad f|| < tolerance - Check function value decrease - Watch for oscillation (step size too large) - `sympy_compu

What's inside
Steps it walks through
  1. When to Use
  2. Decision Tree
  3. Tool Commands
  4. ScipyBfgs
  5. ScipyCg
  6. SympyGradient
  7. Key Techniques
  8. Cognitive Tools Reference
Commands it runs
uv run python -c "from scipy.optimize import minimize; res = minimize(lambda x: x[0]**2 + x[1]**2, [1, 1], method='CG'); print('Min at', res.x)"
uv run python -m runtime.harness scripts/sympy_compute.py diff "x**2 + y**2" --var "[x, y]"
More from Continuous-Claude-v3
All skills →
About this skill
What does the gradient-methods skill do?

Problem-solving strategies for gradient methods in optimization

How do I install it?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill gradient-methods --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.

Keep going