clean-codebase

clean-codebase

When to Use

Use this skill when a codebase has accumulated hygiene debt:

• Lint warnings have piled up during rapid development
• Unused imports and variables clutter files
• Dead code paths exist but were never removed
• Formatting is inconsistent across files
• Static analysis tools report fixable issues

Do NOT use for architectural refactoring, bug fixes, or business logic changes. This skill focuses purely on hygiene and automated cleanup.

Inputs

Parameter	Type	Required	Description
codebase_path	string	Yes	Absolute path to codebase root
language	string	Yes	Primary language (js, python, r, rust, etc.)
cleanup_mode	enum	No	safe (default) or aggressive
run_tests	boolean	No	Run test suite after cleanup (default: true)
backup	boolean	No	Create backup before deletion (default: true)

Procedure

Step 1: Pre-Cleanup Assessment

Measure the current state to quantify improvements later.

# Count lint warnings by severity
lint_tool --format json > lint_before.json

# Count lines of code
cloc . --json > cloc_before.json

# List unused symbols (language-dependent)
# JavaScript/TypeScript: ts-prune or depcheck
# Python: vulture
# R: lintr unused function checks

Expected: Baseline metrics saved to lint_before.json and cloc_before.json

On failure: If lint tool not found, skip automated fixes and focus on manual review

Step 2: Fix Automated Lint Warnings

Apply safe automated fixes (spacing, quotes, semicolons, trailing whitespace).

JavaScript/TypeScript:

eslint --fix .
prettier --write .

Python:

black .
isort .
ruff check --fix .

R:

Rscript -e "styler::style_dir('.')"

Rust:

cargo fmt
cargo clippy --fix --allow-dirty

Expected: All safe lint warnings resolved; files formatted consistently

On failure: If automated fixes introduce test failures, revert changes and escalate

Step 3: Identify Dead Code Paths

Use static analysis to find unreferenced functions, unused variables, and orphaned files.

JavaScript/TypeScript:

ts-prune | tee dead_code.txt
depcheck | tee unused_deps.txt

Python:

vulture . | tee dead_code.txt

R:

Rscript -e "lintr::lint_dir('.', linters = lintr::unused_function_linter())"

General approach:

1. Grep for function definitions
2. Grep for function calls
3. Report functions defined but never called

Expected: dead_code.txt lists unused functions, variables, and files

On failure: If static analysis tool unavailable, manually review recent commit history for orphaned code

Step 4: Remove Unused Imports

Clean up import blocks by removing references to packages never used.

JavaScript:

eslint --fix --rule 'no-unused-vars: error'

Python:

autoflake --remove-all-unused-imports --in-place --recursive .

R:

# Manual review: grep for library() calls, check if package used
grep -r "library(" . | cut -d: -f2 | sort | uniq

Expected: All unused import statements removed

On failure: If removing imports breaks build, they were used indirectly — restore and document

Step 5: Remove Dead Code (Mode-Dependent)

Safe Mode (default):

• Only remove code explicitly marked as deprecated
• Remove commented-out code blocks (if >10 lines and >6 months old)
• Remove TODO comments referencing completed issues

Aggressive Mode (opt-in):

• Remove all functions identified as unused in Step 3
• Remove private methods with zero references
• Remove feature flags for deprecated features

For each candidate deletion:

1. Verify zero references in codebase
2. Check git history for recent activity (skip if modified in last 30 days)
3. Remove code and add entry to CLEANUP_LOG.md

Expected: Dead code removed; CLEANUP_LOG.md documents all deletions

On failure: If uncertain whether code is truly dead, move to archive/ directory instead

Step 6: Normalize Formatting

Ensure consistent formatting across all files (even if not caught by linters).

1. Normalize line endings (LF vs CRLF)
2. Ensure single newline at end of file
3. Remove trailing whitespace
4. Normalize indentation (spaces vs tabs, indent width)

# Example: Fix line endings and trailing whitespace
find . -type f -name "*.js" -exec sed -i 's/\r$//' {} +
find . -type f -name "*.js" -exec sed -i 's/[[:space:]]*$//' {} +

Expected: All files follow consistent formatting conventions

On failure: If sed breaks binary files, skip and document

Step 7: Run Tests

Validate that cleanup didn't break functionality.

# Language-specific test command
npm test              # JavaScript
pytest                # Python
R CMD check           # R
cargo test            # Rust

Expected: All tests pass (or same failures as before cleanup)

On failure: Revert changes incrementally to identify breaking change, then escalate

Step 8: Generate Cleanup Report

Document all changes for review.

# Codebase Cleanup Report

**Date**: YYYY-MM-DD
**Mode**: safe | aggressive
**Language**: <language>

## Metrics

| Metric | Before | After | Change |
|--------|--------|-------|--------|
| Lint warnings | X | Y | -Z |
| Lines of code | A | B | -C |
| Unused imports | D | 0 | -D |
| Dead functions | E | F | -G |

## Changes Applied

1. Fixed X lint warnings (automated)
2. Removed Y unused imports
3. Deleted Z lines of dead code (see CLEANUP_LOG.md)
4. Normalized formatting across W files

## Escalations

- [Issue description requiring human review]
- [Uncertain deletion moved to archive/]

## Validation

- [x] All tests pass
- [x] Backup created: backup_YYYYMMDD/
- [x] CLEANUP_LOG.md updated

Expected: Report saved to CLEANUP_REPORT.md in project root

On failure: (N/A — generate report regardless of outcome)

Validation Checklist

After cleanup:

• All tests pass (or same failures as before)
• No new lint warnings introduced
• Backup created before any deletions
• CLEANUP_LOG.md documents all removed code
• Cleanup report generated with metrics
• Git diff reviewed for unexpected changes
• CI pipeline passes

Common Pitfalls

1. Removing Code Still Used via Reflection: Static analysis misses dynamic calls (e.g., eval(), metaprogramming). Always check git history.

2. Breaking Implicit Dependencies: Removing imports that were used by dependencies. Run tests after every import removal.

3. Deleting Feature Flags for Active Features: Even if unused in current branch, feature flags may be active in other environments. Check deployment configs.

4. Over-Aggressive Formatting: Tools like black or prettier may reformat code in ways that trigger unnecessary diffs. Configure tools to match project style.

5. Ignoring Test Coverage: Cannot safely clean codebases without tests. If coverage is low, escalate for test additions first.

6. Not Backing Up: Always create backup_YYYYMMDD/ directory before deleting anything, even if using git.

7. Wrong R binary on hybrid systems: On WSL or Docker, Rscript may resolve to a cross-platform wrapper instead of native R. Check with which Rscript && Rscript --version. Prefer the native R binary (e.g., /usr/local/bin/Rscript on Linux/WSL) for reliability. See Setting Up Your Environment for R path configuration.

Related Skills

• tidy-project-structure — Organize directory layout, update READMEs
• repair-broken-references — Fix dead links and imports
• escalate-issues — Route complex problems to specialists
• r-packages/run-r-cmd-check — Run full R package checks
• devops/dependency-audit — Check for outdated dependencies