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Code Review

Checked
rtgs2017·vlatest·backend·AGPL-3.0

Analyzes Python code for common errors, style issues, and potential improvements based on RTGS 2017 guidelines.

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 rtgs2017/nagaagent --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

rtgs2017

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 rtgs2017/nagaagent at c2caa9079b.

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

Commit
c2caa9079b
Manifest
skills/code-review/SKILL.md
Scanned
1 file, 71 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 code-review skill enables AI agents to analyze source code, identify logical errors, and suggest improvements based on best practices. It supports multiple programming languages and integrates with version control systems for streamlined development workflows.

When to use it

  • Reviewing pull requests before merging to catch bugs early.
  • Refactoring legacy codebases to improve maintainability and performance.
  • Onboarding new developers by explaining complex logic in existing modules.
  • Validating security patterns and dependency usage in backend services.

Key capabilities

  • Multi-language support for various programming environments.
  • Integration with Git repositories for seamless workflow automation.
  • Automated detection of common coding anti-patterns and vulnerabilities.
  • Context-aware suggestions tailored to project-specific standards.

Example prompts

  • "Review this Python function for potential race conditions and suggest fixes."
  • "Analyze the provided JavaScript module for security vulnerabilities and performance bottlenecks."
  • "Generate a summary of changes in this pull request and highlight any breaking updates."

Tips & gotchas

Ensure your AI agent has access to the relevant codebase or file paths before initiating a review. For large projects, consider breaking down reviews into smaller, focused tasks to maintain accuracy and relevance.