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.

• Run the stats script:

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

• 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