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Tech Debt

Official
anthropics·vlatest·backend·Apache-2.0

This skill analyzes code to identify potential technical debt issues, helping developers prioritize refactoring and improve codebase health.

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 anthropics/knowledge-work-plugins --skill 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

anthropics

Matched to the vendor's own GitHub organisation.

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 anthropics/knowledge-work-plugins at da38ec1ee8.

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

Commit
da38ec1ee8
Manifest
engineering/skills/tech-debt/SKILL.md
Scanned
1 file, 33 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 helps AI agents systematically identify, categorize, and prioritize technical debt within a codebase. It analyzes code to pinpoint issues like duplicated logic or outdated dependencies, then assesses their impact, risk, and effort for remediation. The output is a prioritized list with estimated effort, business justification, and a phased plan that can be integrated into ongoing development work.

When to use it

  • When you need help identifying areas of your codebase that are hindering developer productivity.
  • To proactively address potential risks associated with technical debt, such as security vulnerabilities or scaling limitations.
  • For prioritizing refactoring efforts alongside new feature development.
  • When onboarding new team members and needing to understand the current state of the codebase.
  • To create a remediation plan for addressing identified technical debt items.

Key capabilities

  • Identifies various types of technical debt, including code, architecture, test, dependency, documentation, and infrastructure debt.
  • Categorizes debt based on examples like duplicated logic or missing runbooks.
  • Prioritizes debt using a framework considering Impact (1-5), Risk (1-5), and Effort (1-5).
  • Calculates priority score: (Impact + Risk) x (6 - Effort)
  • Generates prioritized lists with estimated effort, business justification, and phased remediation plans.

Example prompts

  • "Analyze this code for technical debt and prioritize the issues."
  • "What are the biggest risks associated with our current architecture?"
  • "Generate a plan to address our documentation debt over the next quarter."

Tips & gotchas

  • The accuracy of the analysis depends on the quality and completeness of the codebase provided.
  • Effort estimation is subjective and may require developer input for refinement.
  • Consider integrating remediation tasks into existing feature work to avoid disrupting development flow.