Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add parcadei/Continuous-Claude-v3 --skill "tldr-stats"
Install specific skill from multi-skill repository
# Description
Show full session token usage, costs, TLDR savings, and hook activity
# SKILL.md
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.
- 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
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.