tldr-stats▌
parcadei/continuous-claude-v3 · updated Apr 8, 2026
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Show a beautiful dashboard with token usage, actual API 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
How to use tldr-stats on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add tldr-stats
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches tldr-stats from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate tldr-stats. Access the skill through slash commands (e.g., /tldr-stats) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★53 reviews- ★★★★★Daniel Thomas· Dec 20, 2024
Useful defaults in tldr-stats — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Piyush G· Dec 16, 2024
tldr-stats reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Xiao Liu· Dec 16, 2024
tldr-stats is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Aanya Khanna· Dec 12, 2024
tldr-stats has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Charlotte Torres· Dec 8, 2024
tldr-stats reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Daniel Menon· Nov 27, 2024
I recommend tldr-stats for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Noor Abbas· Nov 11, 2024
Registry listing for tldr-stats matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Dhruvi Jain· Nov 7, 2024
I recommend tldr-stats for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aanya Reddy· Nov 7, 2024
Solid pick for teams standardizing on skills: tldr-stats is focused, and the summary matches what you get after install.
- ★★★★★William Chawla· Nov 7, 2024
tldr-stats fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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