autoai-debtmap 0.1.0

Technical Debt Intelligence — connect code quality to business impact

DebtMap — Technical Debt Intelligence

Connect code quality to business impact. Only 7% of engineers use ANY tool for technical debt measurement. DebtMap is the tool for the other 93%.

What It Does

DebtMap scans your codebase and produces actionable intelligence:

• 7 analyzers: Complexity, coupling, duplication, dead code, test gaps, dependency age, documentation gaps
• Business impact scoring: Connects code issues to incidents, velocity, and dollar cost
• ROI prioritization: "Fix this module first — saves $X/month in engineering time"
• Trend tracking: Is your debt improving or degrading over time?
• Executive reports: Share with leadership in a format they understand

Install

pip install autoai-debtmap

For MCP server support:

pip install 'autoai-debtmap[mcp]'

CLI Usage

# Full scan
debtmap scan /path/to/repo

# Get prioritized fix list
debtmap prioritize /path/to/repo

# Generate executive report
debtmap report /path/to/repo

# Find worst hotspots
debtmap hotspots /path/to/repo

# Estimate $ cost of debt
debtmap cost /path/to/repo --hourly-rate 100 --team-size 8

# Track trends over time
debtmap trend /path/to/repo

Quick Start -- MCP Server

Add to your Claude Code or Cursor MCP config:

{
  "mcpServers": {
    "debtmap": {
      "command": "uvx",
      "args": ["autoai-debtmap-mcp"],
      "description": "DebtMap — Scan your codebase for technical debt with ROI-ranked fix lists"
    }
  }
}

That's it. No signup. No API key. No data leaves your machine.

MCP Tools

Tool	Description
debt_scan	Scan a codebase for technical debt
debt_hotspots	Find worst code hotspots
debt_prioritize	ROI-ranked fix list
debt_cost	Estimate $ cost of debt
debt_trend	Debt trend over time
debt_report	Executive-ready report
debt_compare	Compare branches/releases

Analyzers

Cyclomatic Complexity

Parses Python AST to calculate real cyclomatic complexity. Counts if, elif, for, while, except, with, and, or, assert, ternary expressions, and comprehensions.

Module Coupling

Builds an import dependency graph and calculates afferent/efferent coupling and instability metrics. Detects circular dependencies.

Code Duplication

Uses content-hash fingerprinting with normalization (whitespace, string/number literals) to detect near-duplicate code blocks.

Dead Code Detection

Identifies unused functions, unused imports, and unreachable code after return/raise/break/continue statements.

Test Coverage Gaps

Cross-references source module public APIs with test files to find untested functions, classes, and methods.

Dependency Age

Parses requirements.txt and pyproject.toml, queries PyPI for latest versions, and classifies outdatedness severity.

Documentation Gaps

Finds public functions, classes, methods, and modules missing docstrings.

License

Apache 2.0