tldr-stats
Generates a dashboard showing token usage, costs, and savings. Tracks TLDR performance and model expenditure.
Install
mkdir -p .claude/skills/tldr-stats && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/3319" && unzip -o skill.zip -d .claude/skills/tldr-stats && rm skill.zipInstalls to .claude/skills/tldr-stats
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Show full session token usage, costs, TLDR savings, and hook activityKey capabilities
- →Calculate total session token usage
- →Estimate financial cost per session
- →Analyze TLDR compression ratios
- →Identify cache hit efficiency
How it works
It executes a Python diagnostic script that parses local logs to aggregate token consumption metrics and compare them against baseline estimates.
Inputs & outputs
When to use tldr-stats
- →Reviewing API spend for a session
- →Comparing token savings
- →Debugging TLDR hook activity
- →Checking model usage efficiency
About this skill
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
When not to use it
- →Calculating costs across multiple non-connected sessions
- →Deep debugging of individual model logic
Prerequisites
Limitations
- →Limited to local session history
- →Requires script execution permission
How it compares
It provides immediate real-time financial transparency for AI usage rather than relying on delayed cloud provider reporting.
Compared to similar skills
tldr-stats side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| tldr-stats (this skill) | 1 | 6mo | Review | Beginner |
| agent-session-monitor | 2 | 6mo | Review | Intermediate |
| quant-analyst | 103 | 2mo | No flags | Advanced |
| stock-analyzer | 71 | 2mo | Review | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by parcadei
View all by parcadei →You might also like
agent-session-monitor
alibaba
Real-time agent conversation monitoring - monitors Higress access logs, aggregates conversations by session, tracks token usage. Supports web interface for viewing complete conversation history and costs. Use when users ask about current session token consumption, conversation history, or cost statistics.
quant-analyst
zenobi-us
Expert quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and high-frequency trading with focus on mathematical rigor, performance optimization, and profitable strategy development.
stock-analyzer
FrancyJGLisboa
Provides comprehensive technical analysis for stocks and ETFs using RSI, MACD, Bollinger Bands, and other indicators. Activates when user requests stock analysis, technical indicators, trading signals, or market data for specific ticker symbols.
google-analytics
davila7
Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.
data-engineering
pluginagentmarketplace
ETL pipelines, Apache Spark, data warehousing, and big data processing. Use for building data pipelines, processing large datasets, or data infrastructure.
math-tools
ananddtyagi
Deterministic mathematical computation using SymPy. Use for ANY math operation requiring exact/verified results - basic arithmetic, algebra (simplify, expand, factor, solve equations), calculus (derivatives, integrals, limits, series), linear algebra (matrices, determinants, eigenvalues), trigonometry, number theory (primes, GCD/LCM, factorization), and statistics. Ensures mathematical accuracy by using symbolic computation rather than LLM estimation.