Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nicepkg/ai-workflow/analyticsnpx skills add nicepkg/ai-workflow --skill analyticsgit clone --depth 1 https://github.com/nicepkg/ai-workflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nicepkg/ai-workflow/analytics)<a href="https://agentmods.dev/skills/nicepkg/ai-workflow/analytics"><img src="https://agentmods.dev/badge/skills/nicepkg/ai-workflow/analytics.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00033 | $0.02264 |
| Opus 5 | $0.00016 | $0.01132 |
| Sonnet 5 | $0.00007 | $0.00453 |
| Haiku 4.5 | $0.00003 | $0.00226 |
Grade A, and why
analytics-metrics-kpi scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics & Metrics Skill
Become data-driven. Define meaningful metrics, build dashboards, run experiments, and make decisions based on data, not intuition.
Metrics Framework (Acquisition → Revenue)
North Star Metric
Definition: One metric that best captures the value your product delivers.
Characteristics:
- Directly tied to business success
- Driven by product improvements
- Leading indicator of revenue
- Understandable to whole company
Examples:
- Slack: Daily Active Users (DAU)
- Airbnb: Booked Nights
- YouTube: Watch Time
- Uber: Rides Completed
- Stripe: Payment Volume Processed
Funnel Metrics (Acquisition)
Total Visitors: 100,000/month
↓ 20% conversion
Free Signups: 20,000
↓ 10% free-to-paid
Paid Customers: 2,000
CAC: $50 (marketing + sales spend / customers acquired)
LTCAC: $100 (all customer acquisition costs)
Metrics to Track:
- Traffic - Total visitors to website/app
- Signup Rate - % who sign up (target: 10-15%)
- Free-to-Paid Conversion - % free users who pay (target: 2-5%)
- CAC - Cost per acquired customer
- CAC Payback - Months to recover CAC from revenue (target: < 12 months)
Activation Metrics
Goal: New users become active users
Free Signups: 2,000
↓ 30% onboard successfully
Activated: 600
↓ 60% remain active Day 7
Day 7 Active: 360
Metrics to Track:
- Onboarding Completion Rate - % who complete setup (target: 50-80%)
- Time to First Value - Hours to first successful use
- Feature Adoption - % who try key features
- Day 1/7/30 Retention - % active those days (target: 40/25/15)
Engagement Metrics
Goal: Users regularly use product
Daily/Monthly Metrics:
- DAU/MAU - Daily/Monthly Active Users
- DAU/MAU Ratio - Stickiness (target: 20-30%)
- Feature Usage - % using key features
- Session Length - Minutes per session
- Session Frequency - Times per week
Cohort Analysis Example:
Jan Cohort (1,000 signups):
- Day 1: 600 active (60%)
- Day 7: 360 active (36%)
- Day 30: 180 active (18%)
- Month 3: 90 active (9%)
Feb Cohort (1,500 signups):
- Day 1: 1050 active (70%) ← Improving!
- Day 7: 630 active (42%)
- Day 30: 300 active (20%)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 355 lines · 33 tokens per session scan A 4f37ea86c128
analytics-metrics-kpi is a skill published in the GitHub repository nicepkg/ai-workflow (282 stars, last pushed 7mo ago), licensed MIT. It adds 33 tokens to every session and 2,264 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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