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Agent skill

Product Analytics

by majiayu000 in majiayu000/spellbook

Product analytics and growth expert. An agent skill from majiayu000/spellbook.

MITAuto-check passedData & Analytics

Install Product Analytics

skills CLI
$ npx skills add majiayu000/spellbook --skill product-analytics -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install majiayu000/spellbook product-analytics --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/majiayu000/spellbook.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/product-analytics .claude/skills/product-analytics && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
product-analytics
GitHub stars
287
Token cost
~2.7k tokens
SKILL.md length
550 words
Files
7
Skills in repo
97
Repo updated
First seen
Licence
MIT

At a glance

Product analytics and growth expert. An agent skill from majiayu000/spellbook.

  • Works in 5 steps: Acquisition → Activation → Retention → …
  • Designing event tracking
  • SKILL.md covers Core Principles, Hard Rules (Must Follow), Quick Reference and North Star Metric, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Product Analytics is an agent skill from majiayu000/spellbook. Product analytics and growth expert. Use when designing event tracking, defining metrics, running A/B tests, or analyzing retention. Covers AARRR framework, funnel analysis, cohort analysis, and experimentation.

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files (for example `reference/event-tracking.md`, `reference/experimentation.md` and `reference/extended.md`).

It sits in Data & Analytics, covering Product analytics, Product metrics and A/B testing. The repository describes itself as: Cross-runtime skills for Claude Code, Codex, and multi-agent workflows. The licence is MIT.

When your agent uses it

  • Designing event tracking
  • Defining metrics
  • Running A/B tests
  • Analyzing retention

Example prompts

  • “/product-analytics”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Acquisition
  2. Activation
  3. Retention
  4. Referral
  5. Revenue

What it can do on your machine

Read from SKILL.md and the folder at commit ed52af7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Product Analytics loads about 2.7k tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 550 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~57
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from majiayu000/spellbook at commit ed52af7, republished under its MIT licence (© majiayu000). 550 words, ~2,709 tokens.

Download SKILL.mdSave it as .claude/skills/product-analytics/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
product-analytics
description
Product analytics and growth expert. Use when designing event tracking, defining metrics, running A/B tests, or analyzing retention. Covers AARRR framework, funnel analysis, cohort analysis, and experimentation.

Product Analytics

Core Principles

  • Metrics over vanity — Focus on actionable metrics tied to business outcomes
  • Data-driven decisions — Hypothesize, measure, learn, iterate
  • User-centric measurement — Track behavior, not just pageviews
  • Statistical rigor — Understand significance, avoid false positives
  • Privacy-first — Respect user data, comply with GDPR/CCPA
  • North Star focus — Align all teams around one key metric

Hard Rules (Must Follow)

These rules are mandatory. Violating them means the skill is not working correctly.

No PII in Events

Events must NEVER contain personally identifiable information.

javascript
// ❌ FORBIDDEN: PII in event properties
track('user_signed_up', {
  email: 'user@example.com',     // PII!
  name: 'John Doe',              // PII!
  phone: '+1234567890',          // PII!
  ip_address: '192.168.1.1',     // PII!
  credit_card: '4111...',        // NEVER!
});

// ✅ REQUIRED: Anonymized/hashed identifiers only
track('user_signed_up', {
  user_id: hash('user@example.com'),  // Hashed
  plan: 'pro',
  source: 'organic',
  country: 'US',                       // Broad location OK
});

// Masking utilities
const maskEmail = (email) => {
  const [name, domain] = email.split('@');
  return `${name[0]}***@${domain}`;
};
Object_Action Event Naming

All event names must follow the object_action snake_case format.

javascript
// ❌ FORBIDDEN: Inconsistent naming
track('signup');                    // No object
track('newProject');                // camelCase
track('Upload File');               // Spaces and PascalCase
track('user-created');              // kebab-case
track('BUTTON_CLICKED');            // SCREAMING_CASE

// ✅ REQUIRED: object_action snake_case
track('user_signed_up');
track('project_created');
track('file_uploaded');
track('payment_completed');
track('checkout_started');
Actionable Metrics Only

Track metrics that drive decisions, not vanity metrics.

javascript
// ❌ FORBIDDEN: Vanity metrics without context
track('page_viewed');               // No insight
track('button_clicked');            // Too generic
track('app_opened');                // Doesn't indicate value

// ✅ REQUIRED: Actionable metrics tied to outcomes
track('feature_activated', {
  feature: 'dark_mode',
  time_to_activation_hours: 2.5,
  user_segment: 'power_user',
});

track('checkout_completed', {
  order_value: 99.99,
  items_count: 3,
  payment_method: 'credit_card',
  coupon_applied: true,
});
Statistical Rigor for Experiments

A/B tests must have proper sample size and significance thresholds.

javascript
// ❌ FORBIDDEN: Drawing conclusions too early
// "After 100 users, variant B has 5% higher conversion!"
// This is not statistically significant.

// ✅ REQUIRED: Proper experiment setup
const experimentConfig = {
  name: 'new_checkout_flow',
  hypothesis: 'New flow increases conversion by 10%',

  // Statistical requirements
  significance_level: 0.05,      // 95% confidence
  power: 0.80,                   // 80% power
  minimum_detectable_effect: 0.10, // 10% lift

  // Calculated sample size
  sample_size_per_variant: 3842,

  // Guardrails
  max_duration_days: 14,
  stop_if_degradation: -0.05,    // Stop if 5% worse
};

Quick Reference

When to Use What
ScenarioFramework/ToolKey Metric
Overall product healthNorth Star MetricTime spent listening (Spotify), Nights booked (Airbnb)
Growth optimizationAARRR (Pirate Metrics)Conversion rates per stage
Feature validationA/B TestingStatistical significance (p < 0.05)
User engagementCohort AnalysisDay 1/7/30 retention rates
Conversion optimizationFunnel AnalysisDrop-off rates per step
Feature impactAttribution ModelingMulti-touch attribution
Experiment successStatistical TestingPower, significance, effect size

North Star Metric

Definition

A North Star Metric is the one metric that best captures the core value your product delivers to customers. When this metric grows sustainably, your business succeeds.

Characteristics of Good NSMs
✓ Captures product value delivery
✓ Correlates with revenue/growth
✓ Measurable and trackable
✓ Movable by product/engineering
✓ Understandable by entire org
✓ Leading (not lagging) indicator
Examples by Company
CompanyNorth Star MetricWhy It Works
SpotifyTime Spent ListeningCore value = music enjoyment
AirbnbNights BookedRevenue driver + value delivered
SlackDaily Active TeamsEngagement = product stickiness
FacebookMonthly Active UsersNetwork effect foundation
AmplitudeWeekly Learning UsersValue = analytics insights
DropboxActive Users Sharing FilesCore product behavior
NSM Framework
North Star Metric
       ↓
┌──────┴──────┬──────────┬──────────┐
│             │          │          │
Input 1    Input 2   Input 3   Input 4
(Supporting metrics that drive NSM)

Example: Spotify
NSM: Time Spent Listening
├── Daily Active Users
├── Playlists Created
├── Songs Added to Library
└── Share/Social Actions
How to Define Your NSM

  1. Identify core value proposition

    • What job does your product do for users?
    • When do users get "aha!" moment?

  2. Find the metric that represents this value

    • Transaction completed? (e.g., Nights Booked)
    • Time engaged? (e.g., Time Listening)
    • Content created? (e.g., Messages Sent)

  3. Validate it correlates with business success

    • Does NSM increase → revenue increases?
    • Can product changes move this metric?

  4. Define supporting input metrics

    • What user behaviors drive NSM?
    • Break into 3-5 key inputs
Show full SKILL.md (192 more words)Show less

AARRR Framework (Pirate Metrics)

Overview

The AARRR framework tracks the customer lifecycle across five stages:

ACQUISITION → ACTIVATION → RETENTION → REFERRAL → REVENUE
Stage Definitions
1. Acquisition

When users discover your product

Key Questions:

  • Where do users come from?
  • Which channels have best quality users?
  • What's the cost per acquisition (CPA)?

Metrics:

• Website visitors
• App installs
• Sign-ups per channel
• Cost per acquisition (CPA)
• Channel conversion rates

Example Events:

javascript
// Landing page view
track('page_viewed', {
  page: 'landing',
  utm_source: 'google',
  utm_medium: 'cpc',
  utm_campaign: 'brand_search'
});

// Sign-up started
track('signup_started', {
  source: 'homepage_cta'
});
2. Activation

When users experience core product value

Key Questions:

  • What's the "aha!" moment?
  • How long to first value?
  • What % reach activation?

Metrics:

• Time to first action
• Activation rate (% completing key action)
• Setup completion rate
• Feature adoption rate

Example "Aha!" Moments:

Slack:     Send 2,000 messages in team
Twitter:   Follow 30 users
Dropbox:   Upload first file
LinkedIn:  Connect with 5 people

Example Events:

javascript
// Activation milestone
track('activated', {
  user_id: 'usr_123',
  activation_action: 'first_project_created',
  time_to_activation_hours: 2.5
});
3. Retention

When users keep coming back

Key Questions:

  • What's Day 1/7/30 retention?
  • Which cohorts retain best?
  • What drives churn?

Metrics:

• Day 1/7/30 retention rate
• Weekly/Monthly active users (WAU/MAU)
• Churn rate
• Usage frequency
• Feature stickiness (DAU/MAU)

Retention Calculation:

Day X Retention = Users returning on Day X / Total users in cohort

Example:
Cohort: 1000 users signed up Jan 1
Day 7: 300 returned
Day 7 Retention = 300/1000 = 30%

Example Events:

javascript
// Daily engagement
track('session_started', {
  user_id: 'usr_123',
  session_count: 42,
  days_since_signup: 15
});
4. Referral

When users recommend your product

Key Questions:

  • What's the viral coefficient (K-factor)?
  • Which users refer most?
  • What referral incentives work?

Metrics:

• Viral coefficient (K-factor)
• Referral rate (% users referring)
• Invites sent per user
• Invite conversion rate
• Net Promoter Score (NPS)

Viral Coefficient:

K = (% users who refer) × (avg invites per user) × (invite conversion rate)

Example:
K = 0.20 × 5 × 0.30 = 0.30

K > 1: Viral growth (each user brings >1 new user)
K < 1: Need paid acquisition

Example Events:

javascript
// Referral actions
track('invite_sent', {
  user_id: 'usr_123',
  channel: 'email',
  recipients: 3
});

track('referral_converted', {
  referrer_id: 'usr_123',
  new_user_id: 'usr_456',
  channel: 'email'
});
5. Revenue

When users generate business value

Key Questions:

  • What's customer lifetime value (LTV)?
  • What's LTV:CAC ratio?
  • Which segments monetize best?

Metrics:

• Monthly Recurring Revenue (MRR)
• Average Revenue Per User (ARPU)
• Customer Lifetime Value (LTV)
• LTV:CAC ratio
• Conversion to paid
• Revenue churn

LTV Calculation:

LTV = ARPU × Gross Margin / Churn Rate

Example:
ARPU: $50/month
Gross Margin: 80%
Churn: 5%/month

LTV = $50 × 0.80 / 0.05 = $800

Healthy LTV:CAC ratio: 3:1 or higher

Example Events:

javascript
// Revenue events
track('subscription_started', {
  user_id: 'usr_123',
  plan: 'pro',
  mrr: 29.99,
  billing_cycle: 'monthly'
});

track('upgrade_completed', {
  user_id: 'usr_123',
  from_plan: 'basic',
  to_plan: 'pro',
  mrr_change: 20.00
});
AARRR Metrics Dashboard
markdown
## Acquisition
- Total visitors: 50,000
- Sign-ups: 2,500 (5% conversion)
- Top channels: Organic (40%), Paid (30%), Referral (20%)

## Activation
- Activated users: 1,750 (70% of sign-ups)
- Time to activation: 3.2 hours (median)
- Activation funnel drop-off: 30% at setup step 2

## Retention
- Day 1: 60%
- Day 7: 35%
- Day 30: 20%
- Churn: 5%/month

## Referral
- K-factor: 0.4
- Users referring: 15%
- Invites per user: 4.2
- Invite conversion: 25%

## Revenue
- MRR: $125,000
- ARPU: $50
- LTV: $800
- LTV:CAC: 4:1
- Conversion to paid: 25%

Extended Reference

Detailed material starting at ## Key Metrics & Formulas has been moved to reference/extended.md to keep this skill concise. Load that reference when the task requires the moved examples, command catalogs, checklists, platform details, or implementation templates.

© majiayu000, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files in skills/product-analytics of majiayu000/spellbook.

  • SKILL.md
  • reference/event-tracking.md
  • reference/experimentation.md
  • reference/extended.md
  • reference/metrics-framework.md
  • reference/retention.md
  • templates/tracking-plan.md

Open the folder on GitHubat commit ed52af7

Compare with similar skills

Product Analytics next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Product Analytics compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Product Analytics this skillmajiayu000/spellbook287—~2.7kAutomated safety check: PassMIT
Data And Funnel Analyticsmanojbajaj95/claude-gtm-plugin105—~2.9kAutomated safety check: PassMIT
Feature Analytics Instrumentation Plannermistralai/mistral-vibe5.1k—~2.2kAutomated safety check: PassApache-2.0
A/B Test Analysisphuryn/pm-skills27k—~893Automated safety check: PassMIT
Analytics Trackingfreekmurze/dotfiles1k12 repos~2kAutomated safety check: PassNone
Analytics Interpretationgustavscirulis/snapgrid1161 repos~4.1kAutomated safety check: PassCustom licence

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Categories

Questions about Product Analytics

What does Product Analytics do?

Product analytics and growth expert. An agent skill from majiayu000/spellbook. Product Analytics is an agent skill from majiayu000/spellbook. Product analytics and growth expert.

When should I use Product Analytics?

Product Analytics fits situations like: designing event tracking; defining metrics; running A/B tests; analyzing retention.

How do I install Product Analytics in Claude Code?

Run `npx skills add majiayu000/spellbook --skill product-analytics -a claude-code`. Or copy the skill folder (skills/product-analytics in majiayu000/spellbook) into .claude/skills/product-analytics in your project. Claude Code loads it when a task matches its description.

How do I install Product Analytics in Codex?

Run `npx skills add majiayu000/spellbook --skill product-analytics -a codex`. Or copy the skill folder (skills/product-analytics in majiayu000/spellbook) into .agents/skills/product-analytics in your project. Codex loads it when a task matches its description.

Can I use Product Analytics in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add majiayu000/spellbook --skill product-analytics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-analytics, .gemini/skills/product-analytics, .github/skills/product-analytics and .opencode/skills/product-analytics in your project.

What does Product Analytics need to run?

SKILL.md names no scripts, command-line tools or credentials: Product Analytics is instructions for the agent only.

Does Product Analytics access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Product Analytics safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Product Analytics use?

Product Analytics is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Product Analytics use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Product Analytics?

Skills that share tags, products or a category with Product Analytics: Data And Funnel Analytics (manojbajaj95/claude-gtm-plugin, 105 stars), Feature Analytics Instrumentation Planner (mistralai/mistral-vibe, 5.1k stars), A/B Test Analysis (phuryn/pm-skills, 27k stars) and Analytics Tracking (freekmurze/dotfiles, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Analytics?

majiayu000 (a GitHub user) maintains it in majiayu000/spellbook, which has 287 GitHub stars. The repository holds 97 skills in this directory. The repository was last updated on October 8, 2026.

Source: majiayu000/spellbook on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.