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Code Review

Checked
shareai-lab·vlatest·backend·MIT

Automates code reviews for ShareAI Lab's backend projects, ensuring quality and adherence to coding standards.

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 shareai-lab/learn-claude-code --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

shareai-lab

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 shareai-lab/learn-claude-code at 0dcafa2ae0.

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

Commit
0dcafa2ae0
Manifest
skills/code-review/SKILL.md
Scanned
1 file, 148 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

This skill automates code reviews for ShareAI Lab's backend projects, ensuring quality and adherence to coding standards. It systematically checks code against a defined checklist covering security vulnerabilities, correctness issues, performance bottlenecks, maintainability concerns, and testing adequacy. The skill provides structured feedback including summaries, identified critical issues with suggested fixes, improvement suggestions, positive notes, and an overall verdict on merge readiness.

When to use it

  • Before merging new code into the codebase.
  • When refactoring existing code for improved quality or performance.
  • As part of a continuous integration/continuous delivery (CI/CD) pipeline.
  • To proactively identify potential security vulnerabilities in backend projects.
  • For routine checks on Python, JavaScript/TypeScript codebases.

Key capabilities

  • Security Checks: Identifies injection vulnerabilities (SQL, command, XSS, template), authentication and authorization flaws, data exposure risks, cryptography weaknesses, and dependency vulnerabilities using tools like npm audit, pip-audit, and cargo audit.
  • Correctness Checks: Looks for logic errors, race conditions, resource leaks, error handling issues, and type safety problems.
  • Performance Analysis: Detects N+1 queries, memory inefficiencies, blocking operations, inefficient algorithms, and missing caching opportunities.
  • Maintainability Assessment: Evaluates code naming conventions, complexity (function length, nesting depth), duplication, dead code, and comment quality.
  • Testing Validation: Checks for test coverage, edge case handling, mocking practices, and assertion quality.
  • Provides Structured Output: Delivers reviews in a standardized format with summaries, critical issues, improvement suggestions, positive notes, and a merge verdict.

Example prompts

  • "Review this Python file: src/user_authentication.py."
  • "Perform a code review on the JavaScript component components/form.tsx focusing on security vulnerabilities."
  • "Analyze this Rust module for performance bottlenecks."

Tips & gotchas

  • The skill uses tools like npm audit, pip-audit, and cargo audit; ensure these are installed and configured in your environment if applicable.
  • The review focuses specifically on the areas outlined in the checklist; it may not catch all possible issues.
  • Pay close attention to "Critical Issues" flagged by the skill, as they represent potential high-impact problems.