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Convex Functions

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waynesutton·vlatest·backend·Apache-2.0

This skill analyzes functions to determine if they are convex, crucial for optimization and understanding stability in various fields.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add waynesutton/builder-skills --skill convex-functions -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

waynesutton

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: api.email.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    api.email.comundeclared

    What it found

    • skills/convex-functions/SKILL.md:152contacts api.email.com
      const response = await fetch("https://api.email.com/send", {

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned waynesutton/convexskills at 8ef49c9667.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
8ef49c9667
Manifest
skills/convex-functions/SKILL.md
Scanned
2 files, 449 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The convex-functions skill enables AI agents to analyze and work with convex functions, which are mathematical functions where the line segment between any two points on the graph lies above or on the graph. This includes identifying convexity, computing derivatives, and solving optimization problems involving convex functions.

When to use it

  • When optimizing a function in machine learning or operations research that is known to be convex.
  • When verifying if a given mathematical function exhibits convex properties for theoretical analysis.
  • During algorithm development where convex optimization techniques are required, such as in support vector machines or linear programming.

Key capabilities

  • Analyze the convexity of functions using second derivatives.
  • Solve optimization problems with convex objective functions.
  • Compute gradients and Hessians for convex function evaluation.

Example prompts

  • "Is the function f(x) = x² + 3x convex?"
  • "Find the minimum value of the convex function f(x) = e^x."
  • "Verify if the given function is convex and compute its Hessian matrix."

Tips & gotchas

  • Ensure that the input functions are differentiable, as many convexity checks rely on derivatives.
  • Be aware that not all functions are convex; some may be concave or neither, which can affect optimization results.