tldr-stats

name	tldr-stats
description	Show full session token usage, costs, TLDR savings, and hook activity

TLDR Stats Skill

Show a beautiful dashboard with token usage, actual API costs, TLDR savings, and hook activity.

When to Use

• See how much TLDR is saving you in real $ terms
• Check total session token usage and costs
• Before/after comparisons of TLDR effectiveness
• Debug whether TLDR/hooks are being used
• See which model is being used

Instructions

IMPORTANT: Run the script AND display the output to the user.

1. Run the stats script:

python3 $CLAUDE_PROJECT_DIR/.claude/scripts/tldr_stats.py

2. Copy the full output into your response so the user sees the dashboard directly in the chat. Do not just run the command silently - the user wants to see the stats.

Sample Output

╔══════════════════════════════════════════════════════════════╗
║  📊 Session Stats                                            ║
╚══════════════════════════════════════════════════════════════╝

  You've spent  $96.52  this session

  Tokens Used
        1.2M sent to Claude
      416.3K received back
       97.8K from prompt cache (8% reused)

  TLDR Savings

    You sent:               1.2M
    Without TLDR:           2.5M

    💰 TLDR saved you ~$18.83
    (Without TLDR: $115.35 → With TLDR: $96.52)

    File reads: 1.3M → 20.9K █████████░ 98% smaller

  TLDR Cache
    Re-reading the same file? TLDR remembers it.
    █████░░░░░░░░░░ 37% cache hits
    (35 reused / 60 parsed fresh)

  Hooks: 553 calls (✓ all ok)
  History: █▃▄ ▇▃▇▆ avg 84% compression
  Daemon: 24m up │ 3 sessions

Understanding the Numbers

Metric	What it means
You've spent	Actual $ spent on Claude API this session
You sent / Without TLDR	Actual tokens vs what it would have been
TLDR saved you	Money saved by compressing file reads
File reads X → Y	Raw file tokens compressed to TLDR summary
Cache hits	How often TLDR reuses parsed file results
History sparkline	Compression % over recent sessions (█ = high)

Visual Elements

• Progress bars show savings and cache efficiency at a glance
• Sparklines show historical trends (█ = high savings, ▁ = low)
• Colors indicate status (green = good, yellow = moderate, red = concern)
• Emojis distinguish model types (🎭 Opus, 🎵 Sonnet, 🍃 Haiku)

Notes

• Token savings vary by file size (big files = more savings)
• Cache hit rate starts low, increases as you re-read files
• Cost estimates use: Opus $15/1M, Sonnet $3/1M, Haiku $0.25/1M
• Stats update in real-time as you work