tokei: Code Statistics Analysis

Always invoke analyzing-code skill for code statistics - do not execute bash commands directly.

Use tokei for fast, accurate code statistics across multiple languages and projects.

Default Strategy

Invoke analyzing-code skill for analyzing code statistics by language. Use when understanding project composition, measuring change impact before refactoring, or generating CI/CD metrics.

Common workflow: analyzing-code skill → other skills (finding-files, querying-json, querying-yaml) for filtering or analyzing-code skill after refactoring to measure impact.

Key Options

Basic Output Control

• -f/--files show individual file statistics
• -l/--languages list supported languages and extensions
• -v/--verbose show unknown file extensions (1-3)
• -V/--version show version information
• --hidden include hidden files
• -c/--columns N set strict column width

Filtering & Exclusion

• -t/--type TYPES filter by language types (comma-separated)
• -e/--exclude PATTERNS exclude files/dirs (gitignore syntax)
• --no-ignore don’t respect ignore files
  • --no-ignore-dot don’t respect .ignore/.tokeignore
  • --no-ignore-vcs don’t respect VCS ignore files
  • --no-ignore-parent don’t respect parent ignore files

Output Formats

• -o/--output FORMAT output format (json|yaml|cbor)
  • Requires compilation with features: cargo install tokei --features all
• -i/--input FILE read from previous tokei run or stdin

Sorting Options

• -s/--sort COLUMN sort by column
  • Possible values: files|lines|blanks|code|comments

Detailed Reference

For comprehensive usage patterns, output formats, language filtering, and integration examples, load tokei guide when needing:

• Advanced language filtering and exclusion
• JSON/YAML output processing
• CI/CD integration patterns
• Docker usage examples
• Performance tuning for large codebases

The guide includes:

• Basic usage and output control options
• Language filtering and exclusion patterns
• Output format configuration (JSON, YAML, CBOR)
• Performance tips for large codebases
• Docker usage and CI/CD integration
• Configuration files and environment variables

Skill Combinations

For Discovery Phase

• finding-files → analyzing-code: Analyze specific file types or directories
• extracting-code-structure → analyzing-code: Get statistics for specific code components
• git diff → analyzing-code: Analyze changed files statistics

For Analysis Phase

• analyzing-code → querying-json/querying-yaml: Parse statistics output for reports
• analyzing-code → fuzzy-selecting: Interactive selection from language breakdown
• analyzing-code → viewing-files: View statistics with formatted output

For Refactoring Phase

• analyzing-code → replacing-text: Update configuration based on code statistics
• analyzing-code-structure → analyzing-code: Measure impact of structural changes
• analyzing-code → searching-text: Find files contributing to specific language statistics

Multi-Skill Workflows

• analyzing-code → finding-files → searching-text → analyzing-code-structure: Statistics-driven refactoring pipeline
• analyzing-code → querying-json → fuzzy-selecting: Interactive language-based file selection
• git workflow: git diff → analyzing-code → finding-files → viewing-files (analyze changes, select files, preview)

Integration Examples

# Get language breakdown and filter for top languages
tokei -o json | jq '.data | to_entries | sort_by(.value.stats.code) | reverse | .[0:5] | .[].key'

# Analyze changes after refactoring
git diff --name-only | xargs tokei --files

# Find and analyze specific components
fd -e py src/components | xargs tokei -t Python