Token Pacing

Token Pacing Calculator

Analyzes current token usage against the reset deadline to provide a specific daily burn rate target.

Why? Optimizes resource utility by ensuring the user neither runs out of tokens too early nor leaves unused budget on the table.

Quick Start

This skill calculates optimal token burn rate from current usage data.

To use this skill, provide:

1. Provider name (Claude, Gemini, Codex, VS Code, Cursor, Windsurf, etc.)
2. Current usage % (or % remaining)
3. Time until reset (for weekly providers)

Example: "I'm at 31% in Claude, resets in 4 days"

Then the skill will:

1. Apply provider profile to normalize metric
2. Calculate pacing (Steps 1-4 below)
3. Output the Token Pacing Report

How to Get Usage Data (Optional)

If you have ai-usage installed, run it for a quick overview of all services. Otherwise, check your provider directly:

Web dashboards:

• Claude/Codex: Provider settings pages
• Gemini: https://console.cloud.google.com/
• Copilot: https://github.com/settings/copilot

Provider Profiles

Auto-detect the provider and apply these defaults:

[!NOTE] Weekly providers (Claude, Gemini, Codex) have personal reset cycles — there's no universal reset day. Ask the user (unless provided): "When does your week reset?"

Metric Normalization

If provider shows "% remaining" (e.g., Codex):
    usage_% = 100 - remaining_%
Else:
    usage_% = reported value

Reset Date Calculation

Weekly providers (Claude, Gemini, Codex):

# Reset day is USER-SPECIFIC — must ask if not provided
# Example: "My Claude resets on Thursdays" or "resets in 3 days"
cycle_days = 7
reset = user's next reset date/time
days_remaining = (reset - now) in days
days_elapsed = cycle_days - days_remaining

Monthly providers (VS Code, Cursor, Windsurf):

# Reset is predictable — 1st of next month
cycle_days = days in current month (28-31)
reset = 1st of next month, 12:00am
days_remaining = (reset - now) in days
days_elapsed = current day of month

[!TIP] For weekly providers, if user doesn't know their reset day, suggest they check their usage dashboard — it usually shows "resets in X days".

Inputs Required

For monthly providers (VS Code, Cursor, Windsurf):

• Just the metric value — reset date is predictable
• Example: "16% in VS Code"

For weekly providers (Claude, Gemini, Codex):

• Metric value + reset info (days remaining or reset day)
• Example: "31% in Claude, resets in 4 days" or "Codex 40% left, resets Thursday"

[!TIP] If user gives weekly provider without reset info, ask: "When does your usage reset? (e.g., 'in 3 days' or 'on Friday')"

[!CAUTION] Ambiguous phrasing: If user says "X% left" for a non-Codex provider, clarify immediately: "Is that X% used or X% remaining?" — this changes the calculation entirely.

Calculation Steps

Perform these calculations directly — no external script needed.

Step 0: Validate Inputs

Before calculating, confirm:

1. Provider is identified → if not, ask
2. Metric type is clear (usage % vs % remaining) → if ambiguous, clarify
3. Reset info is available → for weekly providers, ask if missing

Step 1: Calculate Time Metrics

now = current date/time
reset = reset date/time (from provider profile or user input)
days_remaining = (reset - now) in days (decimal, e.g., 4.4)
cycle_days = 7 (weekly) or days_in_month (monthly)
days_elapsed = cycle_days - days_remaining
time_elapsed_% = (days_elapsed / cycle_days) × 100

Step 2: Calculate Usage Metrics

# Normalize metric based on provider
if provider shows "% remaining":
    usage_% = 100 - reported_value
else:
    usage_% = reported_value

remaining_% = 100 - usage_%
daily_target = remaining_% / days_remaining

Step 3: Determine Status

Compare usage_% to time_elapsed_%:

[!TIP] The ±3% buffer avoids false alarms for minor deviations.

Step 4: Calculate Buffer

buffer_% = time_elapsed_% - usage_%

• Positive buffer = tokens banked (ahead of schedule)
• Negative buffer = tokens owed (behind schedule)

Response Template

Report the following:

## Token Pacing Report

**Provider:** [Provider Name] ([weekly/monthly] reset)
**Status:** [Under Budget / On Track / Over Budget]

| Metric | Value |
|--------|-------|
| Used | X% |
| Time Elapsed | Y% |
| Buffer | ±Z% |
| Remaining | W% over N days |
| Daily Target | D%/day |

[One sentence recommendation based on status]

Recommendation by Status

• Under Budget: "You're saving tokens. You can increase usage to [daily_target]%/day to hit 100% by reset."
• On Track: "You're pacing well. Maintain ~[daily_target]%/day."
• Over Budget: "You're burning fast. Limit usage to [daily_target]%/day to last until reset."

Examples

Example 1: VS Code (Monthly Reset)

Input: "16% in VS Code" (current date: Jan 11)

Provider: VS Code → monthly reset, 1st of next month
cycle_days = 31 (January)
days_elapsed = 11
days_remaining = 20
time_elapsed_% = (11 / 31) × 100 = 35.5%

usage_% = 16%
remaining_% = 84%
daily_target = 84 / 20 = 4.2%/day

buffer_% = 35.5 - 16 = +19.5% (well under budget)
status = Under Budget

Output:

## Token Pacing Report

**Provider:** VS Code (monthly reset)
**Status:** Under Budget

| Metric | Value |
|--------|-------|
| Used | 16% |
| Time Elapsed | 35.5% |
| Buffer | +19.5% |
| Remaining | 84% over 20 days |
| Daily Target | 4.2%/day |

You're saving tokens. Increase to 4.2%/day to fully utilize your allocation.

Example 2: Claude (Weekly Reset)

Input: "31% used in Claude" (current: Jan 10, reset: Jan 15 Wed 9am PT)

Provider: Claude → weekly reset, Wednesday 9am PT
cycle_days = 7
days_remaining = 4.4 days
days_elapsed = 2.6 days
time_elapsed_% = (2.6 / 7) × 100 = 37%

usage_% = 31%
remaining_% = 69%
daily_target = 69 / 4.4 = 15.7%/day

buffer_% = 37 - 31 = +6%
status = Under Budget

Output:

## Token Pacing Report

**Provider:** Claude (weekly reset)
**Status:** Under Budget

| Metric | Value |
|--------|-------|
| Used | 31% |
| Time Elapsed | 37% |
| Buffer | +6% |
| Remaining | 69% over 4.4 days |
| Daily Target | 15.7%/day |

You're saving tokens. You can increase usage to 15.7%/day to hit 100% by reset.

Example 3: Codex (% Remaining Format)

Input: "Codex shows 40% left" (current: Thursday, reset: Sunday 12am)

Provider: Codex → weekly reset, shows % REMAINING (invert!)
usage_% = 100 - 40 = 60%

cycle_days = 7
days_remaining = 2.5 days (Thu to Sun)
days_elapsed = 4.5 days
time_elapsed_% = (4.5 / 7) × 100 = 64%

remaining_% = 40%
daily_target = 40 / 2.5 = 16%/day

buffer_% = 64 - 60 = +4%
status = On Track

Output:

## Token Pacing Report

**Provider:** Codex (weekly reset)
**Status:** On Track

| Metric | Value |
|--------|-------|
| Used | 60% (40% remaining) |
| Time Elapsed | 64% |
| Buffer | +4% |
| Remaining | 40% over 2.5 days |
| Daily Target | 16%/day |

You're pacing well. Maintain ~16%/day.

Example 4: Over Budget (The Spender)

Input: "80% in Gemini" (current: Tuesday, reset: Monday 12am)

Provider: Gemini → weekly reset, Monday 12am PT
cycle_days = 7
days_remaining = 6 days
days_elapsed = 1 day
time_elapsed_% = (1 / 7) × 100 = 14%

usage_% = 80%
remaining_% = 20%
daily_target = 20 / 6 = 3.3%/day

buffer_% = 14 - 80 = -66% (way behind)
status = Over Budget

Output:

## Token Pacing Report

**Provider:** Gemini (weekly reset)
**Status:** Over Budget

| Metric | Value |
|--------|-------|
| Used | 80% |
| Time Elapsed | 14% |
| Buffer | -66% |
| Remaining | 20% over 6 days |
| Daily Target | 3.3%/day |

You're burning fast. Limit usage to 3.3%/day to last until reset.

Example 5: On Track (The Balanced)

Input: "50% in Claude" (mid-week)

Provider: Claude → weekly reset
cycle_days = 7
days_remaining = 3.5 days
days_elapsed = 3.5 days
time_elapsed_% = 50%

usage_% = 50%
remaining_% = 50%
daily_target = 50 / 3.5 = 14.3%/day

buffer_% = 0%
status = On Track

Output:

## Token Pacing Report

**Provider:** Claude (weekly reset)
**Status:** On Track

| Metric | Value |
|--------|-------|
| Used | 50% |
| Time Elapsed | 50% |
| Buffer | 0% |
| Remaining | 50% over 3.5 days |
| Daily Target | 14.3%/day |

You're pacing well. Maintain ~14.3%/day.

The 5pm Rule (Advanced Pacing)

Problem: Standard pacing aims for 100% at the exact reset time. But if your reset is at 11:59 AM and you hit 100% at 11:58 AM, you've perfectly paced... but also can't use tokens all morning. Similarly, evening resets (10 PM) mean you can't use tokens during dinner/evening work.

Solution: Aim to hit 100% by 5:00 PM on the "effective reset day" — this eliminates "dead time" when you'd otherwise be token-limited during productive hours.

Effective Reset Day

Examples:

• Reset at 11:59 AM Wednesday → Target: 5:00 PM Tuesday
• Reset at 10:56 PM Thursday → Target: 5:00 PM Thursday
• Reset at 12:00 AM (midnight) Monday → Target: 5:00 PM Sunday

Adjusted Calculation

When using the 5pm Rule, replace the reset datetime in Step 1:

# Standard calculation
reset = actual_reset_datetime

# 5pm Rule calculation
if reset.time < 17:00:
    effective_reset = (reset.date - 1 day) at 17:00
else:
    effective_reset = reset.date at 17:00

# Then use effective_reset for all calculations
days_remaining = (effective_reset - now) in days

[!TIP] The 5pm Rule is optional but recommended for users who want to maximize productive usage hours. Always mention when using it in the report.

Report Addition for 5pm Rule

When using the 5pm Rule, add to the report:

**Target:** 5:00 PM [Day] (5pm Rule applied)

Quality Guidelines

• Round percentages to 1 decimal place
• Always show both metrics: Time passed % vs % used — this makes pacing intuitive at a glance
• If daily_target > 50%, warn that this is an extremely heavy workload
• If days_remaining < 1, switch to hourly targets:

  hours_remaining = days_remaining × 24
  hourly_target = remaining_% / hours_remaining
  

[!WARNING] If daily_target > 50%/day, add this warning: "This requires very heavy usage. Consider whether you can realistically sustain this pace."

Pre-Calculation Checklist

Before running calculations, verify:

• Provider identified (or asked)
• Metric type confirmed (usage % vs % remaining)
• Reset period known (weekly vs monthly)
• For weekly providers: reset day/time obtained from user
• Ambiguous phrasing clarified ("50% left" → usage or remaining?)

Troubleshooting

Common Mistakes to Avoid