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Team Tech Debt

Checked
catlog22·vlatest·backend·MIT

This skill identifies and prioritizes tech debt within a team’s codebase, boosting efficiency and reducing long-term maintenance challenges.

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 catlog22/claude-code-workflow --skill team-tech-debt -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

catlog22

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 catlog22/claude-code-workflow at 07491b04ac.

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

Commit
07491b04ac
Manifest
.codex/skills/team-tech-debt/SKILL.md
Scanned
24 files, 2,574 lines
Copies in repo
2 — 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 team-tech-debt skill helps AI agents identify, categorize, and prioritize technical debt within a codebase. It can analyze code for potential issues like duplicated code, complex logic, or outdated dependencies. The agent can then generate reports summarizing the findings and suggesting remediation strategies to improve code quality and maintainability.

When to use it

  • Refactoring Planning: Before embarking on a large-scale refactor, use this skill to understand the scope of technical debt and plan accordingly.
  • Code Review Assistance: Integrate with code review processes to automatically flag potential areas of technical debt for human reviewers.
  • Onboarding New Developers: Help new team members quickly grasp the existing codebase's challenges by providing a prioritized list of technical debt items.
  • Sprint Planning: Incorporate tech debt remediation into sprint planning, ensuring continuous improvement alongside feature development.

Key capabilities

  • Code analysis for identifying potential technical debt
  • Categorization of identified issues (e.g., duplicated code, complex logic)
  • Prioritization of technical debt based on severity and impact
  • Generation of reports summarizing findings and suggesting remediation strategies

Example prompts

  • "Analyze this codebase [link to repository] for technical debt and generate a report."
  • "Identify areas of high complexity in the authentication module."
  • "Prioritize technical debt items impacting performance in the payment_processing service."

Tips & gotchas

The accuracy of the analysis depends on the quality and clarity of the codebase. Consider providing context about specific areas of concern or architectural patterns to improve results.