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Agent skill

Constrained Optimization

by parcadei in parcadei/Continuous-Claude-v3

Problem-solving strategies for constrained optimization in optimization

MITAuto-check: notesResearch & Science

Install Constrained Optimization

skills CLI
$ npx skills add parcadei/Continuous-Claude-v3 --skill constrained-optimization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install parcadei/Continuous-Claude-v3 constrained-optimization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/math/optimization/constrained-optimization .claude/skills/constrained-optimization && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
constrained-optimization
GitHub stars
3.9k
Used in
2 other repos
Token cost
~927 tokens
SKILL.md length
376 words
Files
1
Skills in repo
141
Repo updated
First seen
Licence
MIT

At a glance

Problem-solving strategies for constrained optimization in optimization

  • Works in 5 steps: Constraint Classification → Lagrangian Method (Equality Constraints) → KKT Conditions (Inequality Constraints) → …
  • Research & Science work in your project
  • SKILL.md covers When to Use, Decision Tree, Tool Commands and Key Techniques, plus 1 more section
  • Calls uv

What it does

Constrained Optimization is an agent skill from parcadei/Continuous-Claude-v3. Problem-solving strategies for constrained optimization in optimization

Its SKILL.md is about 930 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science. The repository describes itself as: Context management for Claude Code. Hooks maintain state via ledgers and handoffs. MCP execution without context pollution. Agent orchestration with isolated context windows. The licence is MIT.

When your agent uses it

  • Research & Science work in your project

Example prompts

  • “/constrained-optimization”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash, Read

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Constraint Classification
  2. Lagrangian Method (Equality Constraints)
  3. KKT Conditions (Inequality Constraints)
  4. Penalty and Barrier Methods
  5. SciPy Constrained Optimization

What it can do on your machine

Read from SKILL.md and the folder at commit d07ff4b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Constrained Optimization loads about 927 tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 376 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~24
When it runs · the whole SKILL.md, loaded when a task matches
~927

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from parcadei/Continuous-Claude-v3 at commit d07ff4b, republished under its MIT licence (© parcadei). 376 words, ~927 tokens.

Download SKILL.mdSave it as .claude/skills/constrained-optimization/SKILL.md (or your agent's skills folder).
name
constrained-optimization
description
Problem-solving strategies for constrained optimization in optimization
allowed-tools
Bash, Read

Constrained Optimization

When to Use

Use this skill when working on constrained-optimization problems in optimization.

Decision Tree

  1. Constraint Classification

    • Equality: h(x) = 0
    • Inequality: g(x) <= 0
    • Bounds: l <= x <= u

  2. Lagrangian Method (Equality Constraints)

    • L(x, lambda) = f(x) + sum lambda_j * h_j(x)
    • Solve: grad_x L = 0 and h(x) = 0
    • sympy_compute.py solve "grad_L_system"

  3. KKT Conditions (Inequality Constraints)

    • Extend Lagrangian with mu_i for g_i(x) <= 0
    • Complementary slackness: mu_i * g_i(x) = 0
    • z3_solve.py prove "kkt_satisfied"

  4. Penalty and Barrier Methods

    • Penalty: add P(x) = rho * sum max(0, g_i(x))^2
    • Barrier: add B(x) = -sum log(-g_i(x)) for interior point
    • Increase penalty/decrease barrier parameter iteratively

  5. SciPy Constrained Optimization

    • scipy.optimize.minimize(f, x0, method='SLSQP', constraints=cons)
    • constraints = [{'type': 'eq', 'fun': h}, {'type': 'ineq', 'fun': lambda x: -g(x)}]
    • bounds = [(l1, u1), (l2, u2), ...]

Tool Commands

Scipy_Slsqp
bash
uv run python -c "from scipy.optimize import minimize; cons = dict(type='eq', fun=lambda x: x[0] + x[1] - 1); res = minimize(lambda x: x[0]**2 + x[1]**2, [1, 1], method='SLSQP', constraints=cons); print('Min at', res.x)"
Sympy_Lagrangian
bash
uv run python -m runtime.harness scripts/sympy_compute.py solve "[2*x - lam, 2*y - lam, x + y - 1]" --vars "[x, y, lam]"
Z3_Kkt_Satisfied
bash
uv run python -m runtime.harness scripts/z3_solve.py prove "complementary_slackness"

Key Techniques

From indexed textbooks:

  • [nonlinear programming_tif] Conjugate Direction Methods** - Methods involving directions conjugate to each other with respect to a certain quadratic form, enhancing efficiency in finding minima. Quasi-Newton Methods** - Variants of Newton’s method that approximate the Hessian matrix. Nonderivative Methods** - Address optimization methods that don’t require derivative information.
  • [nonlinear programming_tif] Optimization Over a Convex Set** - Focuses on optimization problems constrained within a convex set. Optimality Conditions:** Similar to unconstrained optimization, but within the context of convex sets. Feasible Directions and Conditional Gradient** - Explores methods that ensure feasibility within constraints.
  • [nonlinear programming_tif] In this chapter we consider the constrained optimization problem minimize f(z) subject to z € X, where we assume throughout that: (a) X is a nonempty and convex subset of 2. When dealing with algo- rithms, we assume in addition that X is closed. The function f: %™ — R is continuously differentiable over X.
  • [nonlinear programming_tif] The methods for obtaining lower bounds are elaborated on in Section 5. Lagrangian relaxation method is discussed in detail. This method requires the optimization of nondifferentiable functions, and some of the major relevant algorithms, subgradient and cutting plane methods, will be discussed in Chapter 6.
  • [nonlinear programming_tif] The image depicts a three-dimensional graphical representation, likely related to linear algebra or optimization. Key elements include: - Axes: Three intersecting axes are shown, suggesting a three-dimensional coordinate system. Equation and Constraints**: A linear equation {x | Ax = b, x ≥ 0} is noted, indicating a system or set of constraints.
Show full SKILL.md (9 more words)Show less

Cognitive Tools Reference

See .claude/skills/math-mode/SKILL.md for full tool documentation.

© parcadei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/math/optimization/constrained-optimization of parcadei/Continuous-Claude-v3.

Open the folder on GitHubat commit d07ff4b

Used in 2 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in parcadei/Continuous-Claude-v3, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Read arXiv Paperkarpathy/nanochat58k2 repos~494Automated safety check: PassMIT
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Categories

Questions about Constrained Optimization

What does Constrained Optimization do?

Problem-solving strategies for constrained optimization in optimization. Constrained Optimization is an agent skill from parcadei/Continuous-Claude-v3.

When should I use Constrained Optimization?

Constrained Optimization fits situations like: research & Science work in your project.

How do I install Constrained Optimization in Claude Code?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill constrained-optimization -a claude-code`. Or copy the skill folder (.claude/skills/math/optimization/constrained-optimization in parcadei/Continuous-Claude-v3) into .claude/skills/constrained-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Constrained Optimization in Codex?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill constrained-optimization -a codex`. Or copy the skill folder (.claude/skills/math/optimization/constrained-optimization in parcadei/Continuous-Claude-v3) into .agents/skills/constrained-optimization in your project. Codex loads it when a task matches its description.

Can I use Constrained Optimization in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add parcadei/Continuous-Claude-v3 --skill constrained-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/constrained-optimization, .gemini/skills/constrained-optimization, .github/skills/constrained-optimization and .opencode/skills/constrained-optimization in your project.

What does Constrained Optimization need to run?

Going by SKILL.md and its folder, Constrained Optimization needs the command-line tools its instructions call (uv). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash, Read.

Does Constrained Optimization access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Constrained Optimization safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Constrained Optimization use?

Constrained Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Constrained Optimization use?

About 927 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Constrained Optimization?

Skills that share tags, products or a category with Constrained Optimization: Hypothesis Generation (spacering-net/codeg, 3.8k stars), GitHub Deep Research (bytedance/deer-flow, 83k stars), Nature Paper Card (Yuan1z0825/nature-skills, 46k stars) and Read arXiv Paper (karpathy/nanochat, 58k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Constrained Optimization?

parcadei (a GitHub user) maintains it in parcadei/Continuous-Claude-v3, which has 3,940 GitHub stars. The repository holds 141 skills in this directory. The repository was last updated on January 26, 2026.

Source: parcadei/Continuous-Claude-v3 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.