Axiom Vision

Name: Axiom Vision
Author: charleswiltgen

Checked

charleswiltgen·vlatest·other·MIT

Axiom Vision analyzes images to extract structured data and relationships, boosting insights from visual information for diverse applications.

Repository

Install on your platform

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

Run this in your project

terminal

$ npx skills add charleswiltgen/axiom --skill axiom-vision -a claude-code

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

charleswiltgen

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 12 Oct 2026

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

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 undeclared network calls: passed
Makes no network calls.
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.
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 charleswiltgen/axiom at 802803e9fa.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: 802803e9fa
Manifest: axiom-codex/skills/axiom-vision/SKILL.md
Scanned: 13 files, 10,465 lines
Copies in repo: 3 — all of them scanned

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 axiom-vision skill allows AI agents to process and understand visual information. Specifically, it enables image recognition, object detection within images, and generating descriptions of the content found in an image. This capability extends agent interaction beyond text-based communication into a more visually aware realm.

When to use it

Image Analysis: When you need an AI agent to identify objects or features present in an uploaded photograph (e.g., identifying plant species from a picture).
Accessibility Assistance: To describe images for users with visual impairments, providing alternative text descriptions of online content.
Content Moderation: To automatically flag inappropriate or policy-violating imagery based on detected objects or scenes.
Visual Search Enhancement: To improve search results by incorporating image understanding alongside textual queries.

Key capabilities

Image recognition
Object detection
Image description generation

Example prompts

"Describe the contents of this image." (followed by an image upload)
"What objects are present in this photograph?" (followed by an image upload)
“Identify any animals in this picture.” (followed by an image upload)

Tips & gotchas

The quality of results depends heavily on the clarity and resolution of the input images. Complex or obscured scenes may lead to inaccurate object detection or descriptions.