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/product-analyticsnpx skills add nicepkg/ai-workflow --skill product-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/product-analytics)<a href="https://agentmods.dev/skills/nicepkg/ai-workflow/product-analytics"><img src="https://agentmods.dev/badge/skills/nicepkg/ai-workflow/product-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 | $0.00032 | $0.00985 |
| Opus 5 | $0.00016 | $0.00492 |
| Sonnet 5 | $0.00006 | $0.00197 |
| Haiku 4.5 | $0.00003 | $0.00098 |
Grade A, and why
product-analytics 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 today.
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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Analytics
Measure what matters and make data-driven decisions.
North Star Metric
The ONE metric that represents customer value
Examples:
Slack: Weekly Active Users
Airbnb: Nights Booked
Spotify: Time Listening
Shopify: GMV
Your North Star should: ✅ Represent customer value
✅ Correlate with revenue
✅ Be measurable frequently
✅ Rally the team
Key Metrics Hierarchy
North Star Metric
├── Input Metrics (drive North Star)
│ ├── Acquisition
│ ├── Activation
│ └── Retention
└── KPIs (business health)
├── Revenue
├── Churn
└── LTV
Event Tracking
// Track user actions
analytics.track('Button Clicked', {
button_name: 'signup',
page: 'homepage',
user_id: '123'
})
// Track page views
analytics.page('Homepage', {
referrer: document.referrer,
path: window.location.pathname
})
// Identify users
analytics.identify('user-123', {
email: '[email protected]',
plan: 'pro',
created_at: '2024-01-15'
})
Funnel Analysis
Sign-up Funnel:
1. Land on homepage: 10,000 (100%)
2. Click signup: 2,000 (20%)
3. Fill form: 1,000 (10%)
4. Verify email: 800 (8%)
5. Complete onboarding: 400 (4%)
Insights:
- Biggest drop: Homepage to signup (80% lost)
- Fix: Clarify value prop, add social proof
Cohort Analysis
Week 1 Cohort (Jan 1-7):
- D1: 80% active
- D7: 40% active
- D30: 20% active
Week 2 Cohort (Jan 8-14):
- D1: 85% active (+5%)
- D7: 50% active (+10%)
- D30: 30% active (+10%)
Insight: Onboarding changes improved retention!
Retention Curves
Good Retention:
- D1: 60-80%
- D7: 40-60%
- D30: 30-50%
- Flattening curve (good!)
Bad Retention:
- D1: 40%
- D7: 10%
- D30: 2%
- Steep drop-off (bad!)
Key Metrics to Track
Acquisition
- Traffic sources (organic, paid, referral)
- Cost per click (CPC)
- Conversion rate (visitor → signup)
Activation
- Signup → first core action
- Time to value
- Onboarding completion rate
What ships with it
1 file 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.
- today First seen · 197 lines · 32 tokens per session scan A f9401d923368
product-analytics is a skill published in the GitHub repository nicepkg/ai-workflow (283 stars, last pushed 7mo ago), licensed MIT. It adds 32 tokens to every session and 985 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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