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codebase-classification

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Classify codebases before modification to choose appropriate development approach

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Codebase Classification Skill

Analyze and classify codebases before making changes to ensure appropriate development approach.

Overview

Before modifying any codebase, classify it to determine whether to:

  • Follow existing patterns (Disciplined)
  • Gradually improve while following conventions (Transitional)
  • Propose improvements to legacy patterns (Legacy)
  • Establish best practices from scratch (Greenfield)

Classification Types

1. Disciplined Codebase

Signals:

  • Consistent code style (formatting, naming conventions)
  • Comprehensive test coverage (>70%)
  • Clear module boundaries and interfaces
  • Type hints/annotations throughout
  • Up-to-date dependencies
  • Active CI/CD pipeline
  • Good documentation (README, docstrings)

Approach: Follow existing patterns strictly. Don't introduce new conventions.

2. Transitional Codebase

Signals:

  • Mixed code quality (some areas good, others not)
  • Partial test coverage (30-70%)
  • Some type hints, inconsistent usage
  • Active development with modernization efforts
  • Dependencies somewhat current

Approach: Follow existing conventions in touched areas. Propose improvements for new code.

3. Legacy Codebase

Signals:

  • Inconsistent patterns across the codebase
  • Minimal or no tests (<30% coverage)
  • No type hints
  • Outdated dependencies
  • Complex, undocumented logic
  • Possibly unmaintained

Approach: Be careful with changes. Add tests before modifying. Propose gradual improvements.

4. Greenfield Codebase

Signals:

  • New project (<6 months old)
  • Few files (<20 source files)
  • No established patterns yet
  • Minimal or no tests (but not legacy)
  • Active initial development

Approach: Establish best practices from the start. Set up proper structure, testing, CI.

Quick Classification Checklist

Run this analysis before making significant changes:

1. Check test coverage: Is there a test/ or tests/ directory? How comprehensive?
2. Check type hints: Are functions annotated? Is there py.typed or mypy config?
3. Check CI/CD: Is there .github/workflows/, .gitlab-ci.yml, or similar?
4. Check code style: Is there .pre-commit-config.yaml, ruff.toml, or similar?
5. Check dependencies: When was requirements.txt/pyproject.toml last updated?
6. Check documentation: Is there a comprehensive README? API docs?

Decision Matrix

SignalDisciplinedTransitionalLegacyGreenfield
Test coverage>70%30-70%<30%Varies (new)
Type hintsComprehensivePartialNone/minimalVaries
CI/CDActivePresentNone/brokenMay be new
Code styleConsistentMixedInconsistentEstablishing
DependenciesCurrentSomewhat currentOutdatedLatest
AgeAnyAnyUsually old<6 months

Behavior Guidelines

When Disciplined

  • Study existing patterns before writing new code
  • Match naming conventions exactly
  • Follow established module structure
  • Add tests matching existing test style
  • Don't propose architectural changes without strong justification

When Transitional

  • Follow patterns in the specific area you're modifying
  • Match quality of surrounding code or slightly better
  • Add tests for new functionality
  • Document rationale for any pattern deviations

When Legacy

  • Add tests BEFORE modifying code
  • Make minimal changes to achieve goal
  • Document assumptions and findings
  • Propose improvements as separate follow-up work
  • Be extra careful with untested code paths

When Greenfield

  • Establish best practices immediately
  • Set up proper project structure
  • Configure linting, formatting, type checking
  • Write tests for new functionality
  • Create comprehensive documentation

Examples

Identifying Disciplined Codebase

$ ls -la
pyproject.toml          # Modern packaging
.pre-commit-config.yaml # Style enforcement
mypy.ini                # Type checking
.github/workflows/      # CI/CD

$ wc -l tests/**/*.py
2500 total              # Substantial tests

→ Classification: DISCIPLINED
→ Approach: Follow existing patterns strictly

Identifying Legacy Codebase

$ ls -la
setup.py                # Old-style packaging
requirements.txt        # Pinned 3 years ago
# No tests directory
# No CI configuration

$ grep -r "def " src/ | head -5
def process_data(x):    # No type hints
def handle_input(data): # No docstrings

→ Classification: LEGACY
→ Approach: Careful changes, add tests first

Integration

This skill helps agents:

  1. Avoid imposing new patterns on well-structured codebases
  2. Avoid perpetuating bad patterns in legacy codebases
  3. Make appropriate improvement suggestions
  4. Set up proper structure for new projects

Related

  • Tool: shell (for running analysis commands)
  • Tool: read (for examining codebase structure)