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Track and analyze Cursor usage metrics via admin dashboard: requests, model usage, team productivity, and cost optimization. Triggers on "cursor analytics", "cursor usage", "cursor metrics", "cursor reporting", "cursor dashboard", "cursor ROI".
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill cursor-usage-analytics命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Implement user sign-up and sign-in flows with Clerk. Use when building authentication UI, customizing sign-in experience, or implementing OAuth social login. Trigger with phrases like "clerk sign-in", "clerk sign-up", "clerk login flow", "clerk OAuth", "clerk social login".
Implement session management and middleware with Clerk. Use when managing user sessions, configuring route protection, or implementing token refresh and custom JWT templates. Trigger with phrases like "clerk session", "clerk middleware", "clerk route protection", "clerk token", "clerk JWT".
Configure enterprise SSO, role-based access control, and organization management. Use when implementing SSO integration, configuring role-based permissions, or setting up organization-level controls. Trigger with phrases like "clerk SSO", "clerk RBAC", "clerk enterprise", "clerk roles", "clerk permissions", "clerk organizations".
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基于 SOC 职业分类
| name | cursor-usage-analytics |
| description | Track and analyze Cursor usage metrics via admin dashboard: requests, model usage, team productivity, and cost optimization. Triggers on "cursor analytics", "cursor usage", "cursor metrics", "cursor reporting", "cursor dashboard", "cursor ROI". |
| allowed-tools | Read, Write, Edit, Bash(cmd:*) |
| version | 1.18.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| tags | ["saas","cursor","analytics","dashboard"] |
| compatibility | Designed for Claude Code, also compatible with Codex and OpenClaw |
Track and analyze Cursor usage metrics for Business and Enterprise plans. Covers dashboard metrics, cost optimization, adoption tracking, and ROI measurement.
Access: cursor.com/settings > Team > Usage (Business/Enterprise only)
┌─ Dashboard ────────────────────────────────────────────┐
│ │
│ Total Requests This Month: 12,847 │
│ Fast Requests Remaining: 2,153 / 15,000 │
│ Active Users: 28 / 30 seats │
│ Most Used Model: Claude Sonnet (62%) │
│ │
│ ┌─ Usage Trend ─────────────────────────────────┐ │
│ │ ▆▆▇▇██▇▆▇█▇▇▆▅ │ │
│ │ Mon Tue Wed Thu Fri Sat Sun │ │
│ └───────────────────────────────────────────────┘ │
│ │
│ ┌─ Top Users ───────────────────────────────────┐ │
│ │ alice@co.com 847 requests (Sonnet) │ │
│ │ bob@co.com 623 requests (GPT-4o) │ │
│ │ carol@co.com 591 requests (Auto) │ │
│ └───────────────────────────────────────────────┘ │
│ │
└────────────────────────────────────────────────────────┘
| Metric | What It Measures | Target |
|---|---|---|
| Total requests | All AI interactions (Chat, Composer, Inline Edit) | Growing month-over-month |
| Fast requests | Premium model uses (count against quota) | Stay under monthly limit |
| Slow requests | Queued requests after quota exceeded | Minimize (upgrade if high) |
| Tab acceptances | How often Tab suggestions are accepted | 30-50% acceptance rate is healthy |
| Metric | Healthy | Needs Attention |
|---|---|---|
| Weekly active users | 80%+ of seats | Below 50% of seats |
| Requests per user/day | 5-20 | Below 3 (underutilization) |
| Users with 0 requests (30d) | 0-10% of seats | Above 20% (wasted seats) |
| Model diversity | 2-3 models used |
| Single model only |
| Metric | Calculation |
|---|---|
| Cost per seat | Plan price / active users |
| Cost per request | Total spend / total requests |
| BYOK costs | Sum of API provider invoices |
| Total AI spend | Cursor subscription + BYOK costs |
Each team member gets ~500 fast requests per month (varies by plan). Fast requests are consumed when using premium models (Claude Sonnet/Opus, GPT-4o, o1, etc.).
When quota is exceeded:
1. Default to Auto mode
- Cursor routes simple queries to cheaper models
- Only uses premium models when complexity warrants it
2. Educate team on model selection
- Simple questions → cursor-small or GPT-4o-mini
- Standard coding → GPT-4o or Claude Sonnet
- Hard problems only → Claude Opus, o1 (these burn quota fast)
3. Reduce round-trips
- Write detailed prompts (fewer back-and-forth turns)
- Use @Files instead of @Codebase (less context = faster)
- Start new chats instead of continuing stale ones
4. BYOK for power users
- Heavy users can use their own API keys
- Their requests don't count against team quota
# Cursor Usage Report - [Month Year]
## Summary
- Active users: X / Y seats (X% utilization)
- Total AI requests: X,XXX
- Fast request quota usage: XX%
- Monthly cost: $X,XXX
## Adoption Trends
- New users onboarded: X
- Users showing increased usage: X
- Inactive users (0 requests): X
## Model Usage Distribution
- Claude Sonnet: XX%
- GPT-4o: XX%
- Auto: XX%
- Other: XX%
## Recommendations
- [Scale / optimize / train based on data]
Time saved per developer per day: ~1 hour (conservative estimate)
Working days per month: 22
Developer hourly cost (fully loaded): $75
Monthly time savings per developer: 22 hours × $75 = $1,650
Cursor cost per developer: $40/month (Business)
ROI per developer: $1,650 - $40 = $1,610/month
ROI multiple: 41x
Break-even: developer saves >32 minutes/month
Note: Actual time savings vary. Track team velocity (story points, PRs merged, cycle time) before and after Cursor adoption for data-driven ROI.
1. Run team training session (30 min demo of Chat + Composer)
2. Share the cursor-hello-world skill for hands-on practice
3. Create project rules (.cursor/rules/) so AI gives better results
4. Assign "AI Champion" per team to share tips and answer questions
5. Set a 30-day adoption goal and review progress
1. Review model usage -- are users defaulting to expensive models?
2. Enable Auto mode as team default
3. Train on efficient prompting (fewer turns = fewer requests)
4. Consider BYOK for top 5 users (offloads their usage from team quota)
5. Evaluate upgrading to more seats or Enterprise plan
1. Check if project rules are configured (AI is less useful without them)
2. Verify indexing works (poor @Codebase = poor experience)
3. Look for extension conflicts (GitHub Copilot still enabled?)
4. Survey team for friction points and address them