Technical Debt Visualizer
- 150 installs
- 2 repo stars
- Updated January 25, 2026
- jorgealves/agent_skills
Produce visual summaries of debt hotspots—complex modules, stale tests, tangled dependencies—to prioritize refactor sprints and communicate risk to engineering leads.
About
Technical-debt-visualizer is an agent skill that aggregates code-health signals into visual summaries of where debt concentrates across a codebase. It helps SaaS, API, and CLI teams in operate/iterate cycles prioritize refactors with clearer evidence than ad-hoc grep or anecdotal review.
- Highlights complexity and coupling hotspots
- Translates debt into actionable visual summaries
- Supports sprint planning and engineering reviews
- Surfaces stale or risky areas across the codebase
- Helps justify refactor investment to stakeholders
Technical Debt Visualizer by the numbers
- 150 all-time installs (skills.sh)
- +9 installs in the week ending Aug 2, 2026 (Skillselion tracking)
- Ranked #376 of 1,352 Code Review & Quality skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
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| Installs | 150 |
|---|---|
| repo stars | ★ 2 |
| Last updated | January 25, 2026 |
| Repository | jorgealves/agent_skills ↗ |
What it does
Produce visual summaries of debt hotspots—complex modules, stale tests, tangled dependencies—to prioritize refactor sprints and communicate risk to engineering leads.
Files
Technical Debt Visualizer
Purpose and Intent
The technical-debt-visualizer provides a data-driven view of software quality. It helps engineering leaders and developers prioritize refactoring work by identifying files that are both complex and frequently changed—the "high-interest" technical debt.
When to Use
- Sprint Planning: Run this before a dedicated refactoring sprint to identify the best "ROI" targets.
- Architectural Reviews: Use to visualize the impact of legacy systems on the overall codebase health.
- Due Diligence: Quickly assess the health of a new or acquired repository.
When NOT to Use
- Performance Benchmarking: This tool measures code structure and maintainability, not runtime performance.
- Absolute Complexity Rating: Metrics like cyclomatic complexity are indicators, not absolute rules; some complex logic is unavoidable.
Input and Output Examples
Input
source_path: "./src"
output_format: "markdown"Output
A markdown report highlighting "Hotspots"—files that have high complexity and low test coverage.
Error Conditions and Edge Cases
- No Git History: If run on a non-git directory, the "change frequency" metric will be unavailable.
- Unsupported Languages: Complexity analysis is language-dependent; unknown extensions will be reported with a lower confidence score.
Security and Data-Handling Considerations
- Local Scan: The analysis is performed entirely in memory on the local machine.
- No Execution: The tool uses static analysis; it never runs the code it is analyzing.
name: technical-debt-visualizer
version: 1.0.0
description: Generates a heat-map and metrics report of a repository based on code complexity, lack of tests, and 'TODO/FIXME' density. Use when you need to identify high-risk areas for refactoring or when planning technical debt reduction sprints.
inputs:
source_path:
type: string
description: The root directory of the codebase to analyze.
required: true
exclude_patterns:
type: array
items:
type: string
description: Files or directories to ignore (e.g., vendor, tests).
output_format:
type: string
enum: [markdown, html, json]
default: markdown
outputs:
debt_report:
type: string
description: A visual or structured report highlighting technical debt hotspots.
capabilities:
- Cyclomatic and cognitive complexity analysis.
- Test coverage correlation (if coverage data is provided).
- Sentiment and keyword analysis on comments (TODO, FIXME, HACK, XXX).
- File change frequency analysis via git history to identify 'fragile' files.
constraints:
- Metrics are heuristic-based and may need contextual interpretation.
- Requires git history for 'fragile file' detection.
security:
- Analyzes code structure and comments locally.
- Does not execute the analyzed source code.
examples:
- input:
source_path: "./src"
output:
debt_report: "## Technical Debt Hotspots\n- **src/legacy_utils.py**: High complexity, No tests, Frequent changes."
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