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Overview

The rtk gain command provides comprehensive analytics for tracking your token savings across time periods. View summary statistics, analyze trends with daily/weekly/monthly breakdowns, and export data for external dashboards. Database Location: ~/.local/share/rtk/history.db
Retention Policy: 90 days automatic cleanup
Scope: Global across all projects, worktrees, and Claude sessions
For programmatic access to tracking data (Rust API, CI/CD integration, custom dashboards), see Tracking Architecture.

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Quick Reference

# Default summary view
rtk gain

# Temporal breakdowns
rtk gain --daily          # Day-by-day breakdown
rtk gain --weekly         # Week-by-week aggregation
rtk gain --monthly        # Month-by-month aggregation
rtk gain --all            # Show all breakdowns at once

# Export formats
rtk gain --all --format json > savings.json
rtk gain --all --format csv > savings.csv

# Combined flags
rtk gain --graph --history --quota    # Classic view with extras
rtk gain --daily --weekly --monthly   # Multiple breakdowns

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Command Options

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Temporal Flags

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--daily
flag
Day-by-day breakdown showing all recorded days with full metrics
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--weekly
flag
Week-by-week breakdown aggregated by Sunday-Saturday weeks
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--monthly
flag
Month-by-month breakdown aggregated by calendar month
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--all
flag
Show all time breakdowns (daily + weekly + monthly) combined

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Classic Flags

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--graph
flag
ASCII graph of last 30 days of savings
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--history
flag
Recent 10 commands with timestamps and savings percentages
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--quota
flag
Monthly quota analysis comparing savings to subscription tier limits
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--tier
string
default:"20x"
Quota tier: pro, 5x, or 20x (default: 20x)

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Export Formats

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--format
string
default:"text"
Output format: text (terminal display), json (APIs), or csv (Excel/analysis)

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Output Examples

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Default Summary View

rtk gain

RTK Token Savings (Global Scope)
═══════════════════════════════════════════════════════════

Total commands:    196
Input tokens:      1.3M
Output tokens:     59.2K
Tokens saved:      1.2M (95.6%)
Total exec time:   58.4s (avg 298ms)
Efficiency meter: ████████████████████░░░░ 95.6%

By Command
────────────────────────────────────────────────────────────────────────
  #  Command                  Count   Saved   Avg%    Time  Impact
────────────────────────────────────────────────────────────────────────
 1.  rtk git status              89  355.8K   93.4%  26.7s  ██████████
 2.  rtk vitest run              52  863.7K   96.6%  15.2s  ████████████████
 3.  rtk ls                      23   10.5K   87.2%   2.1s  ██
────────────────────────────────────────────────────────────────────────
Metrics explained:
  • Commands: Number of rtk commands executed
  • Input: Estimated tokens from raw command output
  • Output: Actual tokens after rtk filtering
  • Saved: Input - Output (tokens prevented from reaching LLM)
  • Save%: Percentage reduction (Saved / Input) × 100
  • Time: Total execution time across all runs
  • Impact: Visual bar showing relative token savings

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Daily Breakdown

rtk gain --daily

📅 Daily Breakdown (3 days)
════════════════════════════════════════════════════════════════
Date            Cmds      Input     Output      Saved   Save%
────────────────────────────────────────────────────────────────
2026-01-28        89     380.9K      26.7K     355.8K   93.4%
2026-01-29       102     894.5K      32.4K     863.7K   96.6%
2026-01-30         5        749         55        694   92.7%
────────────────────────────────────────────────────────────────
TOTAL            196       1.3M      59.2K       1.2M   95.6%

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Weekly Breakdown

rtk gain --weekly

📊 Weekly Breakdown (1 weeks)
════════════════════════════════════════════════════════════════════════
Week                      Cmds      Input     Output      Saved   Save%
────────────────────────────────────────────────────────────────────────
01-26 → 02-01              196       1.3M      59.2K       1.2M   95.6%
────────────────────────────────────────────────────────────────────────
TOTAL                      196       1.3M      59.2K       1.2M   95.6%
Week definition: Sunday to Saturday (ISO week starting Sunday at 00:00)

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Monthly Breakdown

rtk gain --monthly

📆 Monthly Breakdown (1 months)
════════════════════════════════════════════════════════════════
Month         Cmds      Input     Output      Saved   Save%
────────────────────────────────────────────────────────────────
2026-01        196       1.3M      59.2K       1.2M   95.6%
────────────────────────────────────────────────────────────────
TOTAL          196       1.3M      59.2K       1.2M   95.6%
Month format: YYYY-MM (calendar month)

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JSON Export

rtk gain --all --format json

{
  "summary": {
    "total_commands": 196,
    "total_input": 1276098,
    "total_output": 59244,
    "total_saved": 1220217,
    "avg_savings_pct": 95.62,
    "total_time_ms": 58400,
    "avg_time_ms": 298
  },
  "daily": [
    {
      "date": "2026-01-28",
      "commands": 89,
      "input_tokens": 380894,
      "output_tokens": 26744,
      "saved_tokens": 355779,
      "savings_pct": 93.41,
      "total_time_ms": 26700,
      "avg_time_ms": 300
    }
  ],
  "weekly": [...],
  "monthly": [...]
}
Use cases:
  • API integration
  • Custom dashboards
  • Automated reporting
  • Data pipeline ingestion

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CSV Export

rtk gain --all --format csv

# Daily Data
date,commands,input_tokens,output_tokens,saved_tokens,savings_pct,total_time_ms,avg_time_ms
2026-01-28,89,380894,26744,355779,93.41,26700,300
2026-01-29,102,894455,32445,863744,96.57,15200,149

# Weekly Data
week_start,week_end,commands,input_tokens,output_tokens,saved_tokens,savings_pct,total_time_ms,avg_time_ms
2026-01-26,2026-02-01,196,1276098,59244,1220217,95.62,58400,298

# Monthly Data
month,commands,input_tokens,output_tokens,saved_tokens,savings_pct,total_time_ms,avg_time_ms
2026-01,196,1276098,59244,1220217,95.62,58400,298
Use cases:
  • Excel analysis
  • Python/R data science
  • Google Sheets dashboards
  • Matplotlib/seaborn plotting

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Understanding Token Savings

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Token Estimation

RTK estimates tokens using text.len() / 4 (4 characters per token average). Accuracy: ±10% compared to actual LLM tokenization (sufficient for trends).

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Savings Calculation

Input Tokens    = estimate_tokens(raw_command_output)
Output Tokens   = estimate_tokens(rtk_filtered_output)
Saved Tokens    = Input - Output
Savings %       = (Saved / Input) × 100

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Typical Savings by Command

| Command | Typical Savings | Mechanism | |---------|----------------|-----------|| | rtk git status | 77-93% | Compact stat format | | rtk vitest run | 94-99% | Show failures only | | rtk lint | 84% | Group by rule | | rtk tsc | 83% | Group by file/error code | | rtk pnpm list | 70-90% | Compact dependencies | | rtk grep | 70% | Truncate + group |

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Analysis Workflows

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Weekly Progress Tracking

# Generate weekly report every Monday
rtk gain --weekly --format csv > reports/week-$(date +%Y-%W).csv

# Compare this week vs last week
rtk gain --weekly | tail -3

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Monthly Cost Analysis

# Export monthly data for budget review
rtk gain --monthly --format json | jq '.monthly[] |
  {month, saved_tokens, quota_pct: (.saved_tokens / 6000000 * 100)}'

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Data Science Analysis

import pandas as pd
import subprocess

# Get CSV data
result = subprocess.run(['rtk', 'gain', '--all', '--format', 'csv'],
                       capture_output=True, text=True)

# Parse daily data
lines = result.stdout.split('\n')
daily_start = lines.index('# Daily Data') + 2
daily_end = lines.index('', daily_start)
daily_df = pd.read_csv(pd.StringIO('\n'.join(lines[daily_start:daily_end])))

# Plot savings trend
daily_df['date'] = pd.to_datetime(daily_df['date'])
daily_df.plot(x='date', y='savings_pct', kind='line')

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Excel Analysis

  1. Export CSV: rtk gain --all --format csv > rtk-data.csv
  2. Open in Excel
  3. Create pivot tables:
    • Daily trends (line chart)
    • Weekly totals (bar chart)
    • Savings % distribution (histogram)

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Dashboard Creation

# Generate dashboard data daily via cron
0 0 * * * rtk gain --all --format json > /var/www/dashboard/rtk-stats.json

# Serve with static site
cat > index.html <<'EOF'
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<canvas id="savings"></canvas>
<script>
fetch('rtk-stats.json')
  .then(r => r.json())
  .then(data => {
    new Chart(document.getElementById('savings'), {
      type: 'line',
      data: {
        labels: data.daily.map(d => d.date),
        datasets: [{
          label: 'Daily Savings %',
          data: data.daily.map(d => d.savings_pct)
        }]
      }
    });
  });
</script>
EOF

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Database Management

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Inspect Raw Data

# Location
ls -lh ~/.local/share/rtk/history.db

# Schema
sqlite3 ~/.local/share/rtk/history.db ".schema"

# Recent records
sqlite3 ~/.local/share/rtk/history.db \
  "SELECT timestamp, rtk_cmd, saved_tokens FROM commands
   ORDER BY timestamp DESC LIMIT 10"

# Total database size
sqlite3 ~/.local/share/rtk/history.db \
  "SELECT COUNT(*),
          SUM(saved_tokens) as total_saved,
          MIN(DATE(timestamp)) as first_record,
          MAX(DATE(timestamp)) as last_record
   FROM commands"

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Backup & Restore

# Backup
cp ~/.local/share/rtk/history.db ~/backups/rtk-history-$(date +%Y%m%d).db

# Restore
cp ~/backups/rtk-history-20260128.db ~/.local/share/rtk/history.db

# Export for migration
sqlite3 ~/.local/share/rtk/history.db .dump > rtk-backup.sql

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Cleanup

# Manual cleanup (older than 90 days)
sqlite3 ~/.local/share/rtk/history.db \
  "DELETE FROM commands WHERE timestamp < datetime('now', '-90 days')"

# Reset all data
rm ~/.local/share/rtk/history.db
# Next rtk command will recreate database

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Integration Examples

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GitHub Actions CI/CD

# .github/workflows/rtk-stats.yml
name: RTK Stats Report
on:
  schedule:
    - cron: '0 0 * * 1'  # Weekly on Monday
jobs:
  stats:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Install rtk
        run: cargo install --path .
      - name: Generate report
        run: |
          rtk gain --weekly --format json > stats/week-$(date +%Y-%W).json
      - name: Commit stats
        run: |
          git add stats/
          git commit -m "Weekly rtk stats"
          git push

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Slack Bot

import subprocess
import json
import requests

def send_rtk_stats():
    result = subprocess.run(['rtk', 'gain', '--format', 'json'],
                           capture_output=True, text=True)
    data = json.loads(result.stdout)

    message = f"""
    📊 *RTK Token Savings Report*

    Total Saved: {data['summary']['total_saved']:,} tokens
    Savings Rate: {data['summary']['avg_savings_pct']:.1f}%
    Commands: {data['summary']['total_commands']}
    """

    requests.post(SLACK_WEBHOOK_URL, json={'text': message})

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Troubleshooting

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No data showing

# Check if database exists
ls -lh ~/.local/share/rtk/history.db

# Check record count
sqlite3 ~/.local/share/rtk/history.db "SELECT COUNT(*) FROM commands"

# Run a tracked command to generate data
rtk git status

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Export fails

# Check for pipe errors
rtk gain --format json 2>&1 | tee /tmp/rtk-debug.log | jq .

# Use release build to avoid warnings
cargo build --release
./target/release/rtk gain --format json

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Incorrect statistics

Token estimation is a heuristic. For precise measurements: 
# Install tiktoken
pip install tiktoken

# Validate estimation
rtk git status > output.txt
python -c "
import tiktoken
enc = tiktoken.get_encoding('cl100k_base')
text = open('output.txt').read()
print(f'Actual tokens: {len(enc.encode(text))}')
print(f'rtk estimate: {len(text) // 4}')
"

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Best Practices

  1. Regular Exports: rtk gain --all --format json > monthly-$(date +%Y%m).json
  2. Trend Analysis: Compare week-over-week savings to identify optimization opportunities
  3. Command Profiling: Use --history to see which commands save the most
  4. Backup Before Cleanup: Always backup before manual database operations
  5. CI Integration: Track savings across team in shared dashboards

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See Also

Discover Command

Find missed savings opportunities by scanning Claude Code session history

Tracking Architecture

SQLite-based tracking system and programmatic access API