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Code Review

Checked
xtone·vlatest·backend

Automates backend code reviews, identifying potential bugs, security vulnerabilities, and style inconsistencies in Python/Java projects.

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 xtone/ai_development_tools --skill code-review -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

xtone

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 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 xtone/ai_development_tools at e3603755a4.

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

Commit
e3603755a4
Manifest
development_workflows/skills/code-review/SKILL.md
Scanned
10 files, 2,319 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

The xtone-code-review skill enables AI agents to perform automated static analysis on source code, identifying potential bugs, security vulnerabilities, and style inconsistencies without requiring manual human intervention. It supports multiple programming languages and integrates directly into development workflows to enforce quality standards before deployment.

When to use it

  • Pre-commit validation: Automatically scan pull requests or staged changes to catch syntax errors and logic flaws early in the cycle.
  • Security audits: Proactively detect common vulnerabilities like SQL injection risks or hardcoded secrets within backend services.
  • Style enforcement: Ensure team-wide adherence to coding conventions, formatting rules, and architectural patterns across large codebases.
  • Legacy modernization: Analyze outdated codebases to identify deprecated libraries, anti-patterns, or refactoring opportunities.

Key capabilities

  • Multi-language support for popular backend stacks (Node.js, Python, Go, Java)
  • Real-time linting and static analysis feedback
  • Security-focused vulnerability scanning
  • Customizable rule sets for team-specific guidelines
  • Integration with CI/CD pipelines for automated gating

Example prompts

  • "Run a full code review on this Node.js API endpoint to check for race conditions and memory leaks."
  • "Analyze the provided Python service module for security vulnerabilities and suggest fixes based on OWASP guidelines."
  • "Review this Go microservice implementation for adherence to our team's concurrency patterns and error handling standards."

Tips & gotchas

Ensure your codebase includes a package.json or equivalent dependency manifest, as the skill may reference external libraries for context-aware analysis. While effective at catching common issues, complex architectural flaws often require human judgment alongside automated reviews.