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Agent skill

Convex Optimization

by parcadei in parcadei/Continuous-Claude-v3

Problem-solving strategies for convex optimization in optimization

MITAuto-check: notes

Install Convex Optimization

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

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

GitHub CLI
$ gh skill install parcadei/Continuous-Claude-v3 convex-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/convex-optimization .claude/skills/convex-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
convex-optimization
GitHub stars
3.9k
Used in
2 other repos
Token cost
~909 tokens
SKILL.md length
402 words
Files
1
Skills in repo
141
Repo updated
First seen
Licence
MIT

At a glance

Problem-solving strategies for convex optimization in optimization

  • Works in 5 steps: Verify Convexity → Problem Classification → Standard Form → …
  • SKILL.md covers When to Use, Decision Tree, Tool Commands and Key Techniques, plus 1 more section
  • Calls uv

What it does

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

Its SKILL.md is about 910 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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.

Example prompts

  • “/convex-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. Verify Convexity
  2. Problem Classification
  3. Standard Form
  4. KKT Conditions (Necessary & Sufficient)
  5. Solve and Verify

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

Convex Optimization loads about 909 tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 402 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~22
When it runs · the whole SKILL.md, loaded when a task matches
~909

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). 402 words, ~909 tokens.

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

Convex Optimization

When to Use

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

Decision Tree

  1. Verify Convexity

    • Objective function: Hessian positive semidefinite?
    • Constraint set: intersection of convex sets?
    • z3_solve.py prove "hessian_psd"

  2. Problem Classification

    TypeSolver
    Linear Programmingscipy.optimize.linprog
    Quadratic Programmingscipy.optimize.minimize(method='SLSQP')
    General ConvexInterior point methods
    SemidefiniteCVXPY with SDP solver

  3. Standard Form

    • minimize f(x) subject to g_i(x) <= 0, h_j(x) = 0
    • Convert max to min by negating
    • Convert >= to <= by negating

  4. KKT Conditions (Necessary & Sufficient)

    • Stationarity: grad L = 0
    • Primal feasibility: g_i(x) <= 0, h_j(x) = 0
    • Dual feasibility: lambda_i >= 0
    • Complementary slackness: lambda_i * g_i(x) = 0
    • z3_solve.py prove "kkt_conditions"

  5. Solve and Verify

    • scipy.optimize.minimize(f, x0, constraints=cons)
    • Check constraint satisfaction
    • Verify solution is global minimum (convex guarantees this)

Tool Commands

Scipy_Linprog
bash
uv run python -c "from scipy.optimize import linprog; res = linprog([-1, -2], A_ub=[[1, 1], [2, 1]], b_ub=[4, 5]); print('Optimal:', -res.fun, 'at x=', res.x)"
Scipy_Minimize
bash
uv run python -c "from scipy.optimize import minimize; res = minimize(lambda x: (x[0]-1)**2 + (x[1]-2)**2, [0, 0]); print('Minimum at', res.x)"
Z3_Kkt
bash
uv run python -m runtime.harness scripts/z3_solve.py prove "kkt_conditions"

Key Techniques

From indexed textbooks:

  • [Additional Exercises for Convex Optimization (with] Finally, there are lots of methods that will do better than this, usually by taking this as a starting point and ‘polishing’ the result after that. Several of these have been shown to give fairly reliable, if modest, improvements. You were not required to implement any of these methods.
  • [Additional Exercises for Convex Optimization (with] K { X = x Ax yi } where e is the p-dimensional vector of ones. This is a polyhedron and thus a convex set. Rm has the form − The residual Aˆx − Describe a heuristic method for approximately solving this problem, using convex optimization.
  • [Additional Exercises for Convex Optimization (with] We then pick a small positive number , and a vector c cT x minimize subject to fi(x) 0, hi(x) = 0, f0(x) ≤ p + . There are dierent strategies for choosing c in these experiments. The simplest is to choose the c’s randomly; another method is to choose c to have the form ei, for i = 1, .
  • [Additional Exercises for Convex Optimization (with] We formulate the solution as the following bi-criterion optimization problem: (J ch, T ther) cmax, cmin, 0, minimize subject to c(t) c(t) a(k) ≤ ≥ t = 1, . T The key to this problem is to recognize that the objective T ther is quasiconvex. The problem as stated is convex for xed values of T ther.
  • [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.
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/convex-optimization of parcadei/Continuous-Claude-v3.

Open the folder on GitHubat commit d07ff4b

Used in 2 other repositories

We found 4 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

Convex Optimization next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Convex Optimization compared with similar skills
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Product Strategyphuryn/pm-skills27k—~1.2kAutomated safety check: PassMIT
Convexopenclaw/clawhub9.5k—~2.4kAutomated safety check: PassMIT
Vc Problem Solvingwithkynam/vibecode-pro-max-kit1.1k2 repos~1.1kAutomated safety check: PassMIT

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Questions about Convex Optimization

What does Convex Optimization do?

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

How do I install Convex Optimization in Claude Code?

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

How do I install Convex Optimization in Codex?

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

Can I use Convex 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 convex-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/convex-optimization, .gemini/skills/convex-optimization, .github/skills/convex-optimization and .opencode/skills/convex-optimization in your project.

What does Convex Optimization need to run?

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

Does Convex 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 Convex 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 Convex Optimization use?

Convex 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 Convex Optimization use?

About 909 tokens (SKILL.md is roughly 3.6k 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 Convex Optimization?

Skills that share tags, products or a category with Convex Optimization: Convex (davila7/claude-code-templates, 32k stars), Good Strategy Bad Strategy (wondelai/skills, 2.4k stars), Product Strategy (phuryn/pm-skills, 27k stars) and Convex (openclaw/clawhub, 9.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Convex 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.