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Lisa Review Project

Checked
codyswanngt·vlatest·backend·MIT

Analyzes project documentation and code for potential risks, inefficiencies, and adherence to best practices.

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 codyswanngt/lisa --skill lisa-review-project -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

codyswanngt

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 codyswanngt/lisa at 394e2c9bc2.

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

Commit
394e2c9bc2
Manifest
.agents/skills/lisa-review-project/SKILL.md
Scanned
3 files, 591 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 lisa-review-project skill provides automated project review capabilities. It can analyze code repositories, identify potential issues based on established coding standards and best practices, and generate reports summarizing findings. This helps improve code quality and reduces the time spent on manual reviews.

When to use it

  • Codebase onboarding: Quickly assess a new codebase for adherence to team standards.
  • Pull request review: Automate initial checks in pull requests before human review.
  • Continuous integration: Integrate into CI/CD pipelines to enforce code quality rules.
  • Refactoring projects: Identify areas of improvement and potential risks during large-scale refactorings.

Key capabilities

  • Automated code analysis
  • Identification of coding standard violations
  • Generation of review reports
  • Repository integration

Example prompts

  • "Review this repository: [repository URL]"
  • "Analyze the 'src' directory for potential issues."
  • "Generate a report highlighting all style guide violations in the project."

Tips & gotchas

The skill requires access to the code repository being reviewed. Ensure appropriate permissions are granted to the AI agent before running analysis.