An AI-enhanced diagnostic tool that scans codebases to identify, categorize, and prioritize technical debt with actionable remediation plans.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install tech-debt-scanner
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install tech-debt-scanner using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Tech Debt Scanner?
The Tech Debt Scanner is a sophisticated analysis tool designed to help development teams maintain high-velocity codebases by identifying hidden risks. It goes beyond simple linting by combining static analysis markers—such as TODOs, complex nesting, and large file sizes—with AI-driven reasoning to assess the true impact of debt on your project. Using Openclaw Skills, this tool enables developers to transform a messy backlog of code smells into a prioritized roadmap with clear effort estimates and risk scores.
By analyzing your project structure and git history, it identifies high-ROI refactoring targets where complexity and high change frequency (churn) intersect. Whether you are preparing for a sprint or auditing a legacy repository, this skill provides the technical truth needed to justify cleanup efforts to stakeholders.
Tech Debt Scanner Use Cases
- Conducting comprehensive code quality audits before quarterly planning.
- Identifying high-risk complexity hotspots that are prone to regression bugs.
- Tracking the age and ownership of legacy TODO and FIXME markers.
- Mapping test coverage gaps in the most frequently modified parts of the codebase.
- Automating dependency health checks to find deprecated or vulnerable packages.
How Tech Debt Scanner Works
- The skill automatically detects the project's programming languages and environment settings.
- Specialized detectors run via ripgrep and shell commands to locate debt markers like HACK, XXX, and DEPRECATED.
- Complexity analysis measures file line counts and logical nesting depth to find brittle code structures.
- Git history analysis calculates churn metrics to see which complex files are modified most often.
- The AI engine processes these findings to assign risk levels, effort estimates (XS-XL), and remediation steps.
- A prioritized report is generated, which can be used for manual review or integrated into CI/CD pipelines.
Tech Debt Scanner Setup
To use this tool within the Openclaw Skills ecosystem, ensure you have ripgrep and git installed on your system. Run the following command to initiate a full scan:
# Run a full technical debt audit
openclaw run tech-debt-scanner scan
# Or scan for specific issues like test gaps
openclaw run tech-debt-scanner tests
Tech Debt Scanner Data Schema & Taxonomy
The Tech Debt Scanner organizes its findings into a structured hierarchy for easy processing:
| Data Point | Description | Metadata Collected |
|---|---|---|
| Debt Markers | Found via regex patterns | Line number, Author, Age (via Git Blame), Category |
| Hotspots | Large or complex files | Line count, Nesting depth, Churn rate (90 days) |
| Dependencies | External package health | Version lag (Major/Minor), Deprecation status |
| Test Coverage | Source-to-test ratio | File size, Untested churn hotspots |
Tech Debt Scanner Advanced Features
- AI-Powered Prioritization: Automatically ranks debt items based on risk, effort, and impact.
- CI/CD Thresholds: Configurable exit codes to fail builds if critical debt items are detected.
- Language Agnostic: Detectors adapt to TypeScript, Python, Go, Java, and more based on file extensions.
- Multi-Format Reporting: Export findings as human-readable Markdown, machine-parseable JSON, or plain text.
- Deep Git Integration: Cross-references code smells with historical change frequency to identify the most dangerous files.
SKILL.md
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