Show a beautiful dashboard with token usage, actual API costs, TLDR savings, and hook activity.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontldr-statsExecute the skills CLI command in your project's root directory to begin installation:
Fetches tldr-stats from parcadei/continuous-claude-v3 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate tldr-stats. Access via /tldr-stats in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Show a beautiful dashboard with token usage, actual API costs, TLDR savings, and hook activity.
IMPORTANT: Run the script AND display the output to the user.
python3 $CLAUDE_PROJECT_DIR/.claude/scripts/tldr_stats.py
╔══════════════════════════════════════════════════════════════╗
║ 📊 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
| 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) |
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
parcadei/continuous-claude-v3
mattpocock/skills
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Useful defaults in tldr-stats — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
tldr-stats reduced setup friction for our internal harness; good balance of opinion and flexibility.
tldr-stats is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
tldr-stats has been reliable in day-to-day use. Documentation quality is above average for community skills.
tldr-stats reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend tldr-stats for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for tldr-stats matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend tldr-stats for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: tldr-stats is focused, and the summary matches what you get after install.
tldr-stats fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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