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Tech Debt Analyzer

Checked
ailabs-393·vlatest·backend·MIT

This tool automatically identifies potential technical debt in your codebase, helping developers prioritize fixes and improve code quality.

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 ailabs-393/ai-labs-claude-skills --skill tech-debt-analyzer -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

ailabs-393

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 ailabs-393/ai-labs-claude-skills at 1a12bc7aad.

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

Commit
1a12bc7aad
Manifest
packages/skills/tech-debt-analyzer/SKILL.md
Scanned
8 files, 2,050 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

The Tech Debt Analyzer skill helps developers systematically identify, analyze, document, and track technical debt within JavaScript/TypeScript codebases. It provides automated analysis tools to detect potential issues like large files, complex functions, deprecated dependencies, and more. The tool generates reports categorizing these debts by severity, enabling teams to prioritize remediation efforts and improve overall code quality.

When to use it

  • When onboarding new developers to a project to quickly identify areas of technical debt.
  • During regular code reviews to proactively surface potential issues.
  • Before undertaking major refactoring or feature development to understand the existing codebase's health.
  • To track and manage technical debt over time, ensuring consistent improvement.
  • When identifying vulnerable packages in a project.

Key capabilities

  • Automated Code Smell Detection: Identifies issues like large files (over 500 lines), complex functions (cyclomatic complexity >10 or length >50 lines), and problematic code comments (TODO, FIXME, etc.).
  • Dependency Analysis: Detects deprecated packages, duplicate functionality, versioning problems, and potential security vulnerabilities in project dependencies.
  • Categorization of Technical Debt: Classifies identified issues by severity (HIGH, MEDIUM, LOW).
  • Report Generation: Creates markdown reports summarizing analysis findings.
  • Framework for Manual Review: Provides guidance on areas to focus on during manual code reviews, including architectural, test, documentation and performance debt.

Example prompts

  • "Analyze the codebase for technical debt indicators."
  • "Run a dependency analysis report for package.json."
  • "Detect code smells in the 'src' directory and output a markdown report."

Tips & gotchas

  • The automated analysis relies on scripts executed via the command line (e.g., python3 scripts/detect_code_smells.py). Ensure Python 3 is installed and the necessary dependencies are available.
  • The tool focuses specifically on JavaScript/TypeScript codebases.
  • Security concerns identified during dependency analysis require external audit data to be fully accurate.