CRAPQuants — Code Quality Analysis Skill

CRAPQuants computes CRAP (Change Risk Anti-Patterns) scores for Python code by combining cyclomatic complexity with test coverage, then enriches results with diagnostic tags from six software engineering books.

CRAP formula: CC² × (1 − coverage/100)³ + CC Threshold: Score above 30 = CRAPpy (high risk to change)

When to Use This Skill

• User asks to analyze code quality, complexity, or maintainability
• User wants to know which functions are risky or need refactoring
• User asks about test coverage gaps or technical debt
• Before a refactoring session — identify what to fix first
• During code review — flag risky functions in a PR
• User asks "is this safe to change?" or "what should I refactor first?"

Prerequisites

Check if CRAPQuants is installed:

pip show radon 2>/dev/null && echo "radon: installed" || echo "radon: MISSING"

If not installed, set up:

python3 -m venv .venv && . .venv/bin/activate
pip install --only-binary :all: radon pydantic structlog rich typer polars

If user has CRAPQuants source, use it directly with PYTHONPATH=src.

Analysis Workflow

Step 1: Discover What to Analyze

Ask the user or determine from context:

• Path: Which directory or file? (default: ./src or ./)
• Coverage: Is there a coverage report? Check for coverage.json, coverage.xml, .coverage, lcov.info
• Level: How deep? (default: standard)

# Check for coverage data
ls coverage.json coverage.xml lcov.info .coverage 2>/dev/null

If no coverage report exists:

# Generate one (if pytest is available)
pytest --cov=src --cov-report=json -q 2>/dev/null

Step 2: Run Analysis

# Standard analysis (CRAP + 6 framework tags)
PYTHONPATH=src python -m crapquants.cli analyze {path} --level standard

# With coverage data
PYTHONPATH=src python -m crapquants.cli analyze {path} -c coverage.json

# Quick mode (CRAP only, fastest)
PYTHONPATH=src python -m crapquants.cli analyze {path} --level quick

# JSON output for programmatic use
PYTHONPATH=src python -m crapquants.cli analyze {path} -f json -o report.json

# Markdown for documentation
PYTHONPATH=src python -m crapquants.cli analyze {path} -f markdown -o report.md

Step 3: Interpret Results

After running, explain results to the user using these thresholds:

CRAP Score:

• 1–10: Clean, low risk
• 11–30: Moderate, manageable
• 31–60: CRAPpy, prioritize for refactoring or testing
• 61+: Critical, dangerous to change

Diagnostic Tags come from six books — always mention the source:

• [Feathers] — testability barriers (from "Working Effectively with Legacy Code")
• [Ousterhout] — design quality issues (from "A Philosophy of Software Design")
• [Hunt & Thomas] — engineering culture (from "The Pragmatic Programmer")
• [Fowler] — code smells with named refactorings (from "Refactoring")
• [Tornhill] — behavioral risk (from "Your Code as a Crime Scene")
• [Ford] — architectural fitness functions (from "Building Evolutionary Architectures")

Step 4: Recommend Actions

For each CRAPpy function, provide actionable guidance:

1. If high CC + low coverage: "Write characterization tests first, then refactor" (Feathers approach)
2. If high CC + high coverage: "Safe to refactor — coverage is your safety net" (Hunt & Thomas)
3. If MONSTER_SNARLED tag: "Use Replace Method with Method Object to untangle state"
4. If EDIT_AND_PRAY tag: "Write characterization tests documenting actual behavior before any change"
5. If NONOBVIOUS tag: "Extract Method to name code sections by intent, add type annotations"

Always prioritize: CRITICAL tags first, then HIGH, then WARNING.

Baseline Regression Workflow

For CI/CD or ongoing tracking:

# Save baseline (do this on main branch)
PYTHONPATH=src python -m crapquants.cli analyze {path} -c coverage.json --save-baseline data/baseline.json

# Compare against baseline (do this on PR branch)
PYTHONPATH=src python -m crapquants.cli analyze {path} -c coverage.json --baseline data/baseline.json

Exit code 1 = regression detected. Use in CI to block merging CRAPpy code.

Security Scanning

CRAPQuants includes AST-based security smell detection (always on):

• eval/exec usage (CWE-95)
• pickle deserialization (CWE-502)
• Hardcoded secrets (CWE-798)
• Shell injection (CWE-78)
• Unsafe YAML loading (CWE-502)

For comprehensive SAST, ensure Semgrep is installed and use --level full.

Common Scenarios

"How risky is this function?" → Run quick analysis on the specific file, report CRAP score and what it means.

"What should I refactor first?" → Run standard analysis on the project, sort by CRAP score descending, recommend top 3 with specific refactoring techniques from the Fowler tag mappings.

"Is this codebase healthy?" → Run standard analysis, report PHS (Pragmatic Health Score 0-100), highlight broken windows and coincidence code.

"Help me prepare for a code review" → Run analysis with markdown output, generate report the reviewer can read. Include the glossary section so reviewers understand the metrics.

"Set up quality gates for CI" → Generate baseline, provide GitHub Actions workflow YAML with SHA-pinned actions (Rule #51), configure SARIF output for GitHub Code Scanning.

Important Notes

• CRAPQuants is pure static analysis — no AI, no network calls, works offline
• Coverage data dramatically improves accuracy — always try to include it
• Without coverage, all functions are assumed 0% covered (pessimistic mode)
• Use --missing-policy optimistic during local dev if no coverage available
• Reports include a "How to Read This Report" glossary — remind users it's there

For detailed framework documentation, see references/ directory.