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Aarrr Metrics

Organization
NVlabs
aarrr-metrics

Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis

Overview

PublisherNVlabs
RepositorySkill2Env
Skill nameaarrr-metrics
Stars
189
Forks
18
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by NVlabs on GitHub. Read the source before you install it.

Installation

Install the Aarrr Metrics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/NVlabs/Skill2Env/tree/main/SkillHub/skills/aarrr-metrics
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NVlabs/Skill2Env.git /tmp/Skill2Env
mkdir -p .claude/skills
cp -r /tmp/Skill2Env/SkillHub/skills/aarrr-metrics .claude/skills/aarrr-metrics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aarrr Metrics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aarrr Metrics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aarrr Metrics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AARRR Pirate Metrics

Apply Dave McClure's AARRR framework to measure and optimize growth through the five stages: Acquisition, Activation, Retention, Revenue, and Referral.

When to Use This Skill

  • Building growth dashboards
  • Identifying funnel bottlenecks
  • Prioritizing growth experiments
  • Reporting to investors
  • Diagnosing growth problems

Methodology Foundation

Based on Dave McClure's AARRR framework (500 Startups), providing:

  • Stage-specific metrics definition
  • Funnel conversion analysis
  • Prioritization framework
  • Experiment design guidance

What Claude Does vs What You Decide

Claude DoesYou Decide
Defines metrics per stageSpecific definitions for your product
Identifies bottlenecksExperiment priorities
Suggests experimentsResource allocation
Creates dashboardsTool selection
Benchmarks performanceAcceptable thresholds

Instructions

Step 1: Define Each Stage

AARRR Stages:

StageQuestionFocus
AcquisitionHow do users find you?Traffic, channels
ActivationDo they have a great first experience?Onboarding, aha moment
RetentionDo they come back?Engagement, habit
RevenueDo they pay?Conversion, monetization
ReferralDo they tell others?Virality, NPS

Step 2: Set Stage-Specific Metrics

Metrics Framework:

StagePrimary MetricSupporting Metrics
AcquisitionCAC, Unique visitorsChannel mix, CTR, CPM
ActivationActivation rateTime to activate, drop-off points
RetentionD7/D30 retentionDAU/MAU, churn rate
RevenueLTV, ARPUConversion rate, ACV
ReferralViral coefficientNPS, referral rate

Step 3: Map Your Funnel

Funnel Template:

Acquisition → Activation → Retention → Revenue → Referral
   100%    →    30%     →    15%    →    5%   →    1%

Calculate conversion rates between each stage.

Step 4: Identify Bottlenecks

Bottleneck Analysis:

Drop-offDiagnosis
Acquisition → ActivationPoor onboarding or wrong traffic
Activation → RetentionNot finding core value
Retention → RevenuePricing or value misalignment
Revenue → ReferralNot delighting paying users

Step 5: Prioritize Experiments

ICE Framework:

  • Impact: How much will it move the metric?
  • Confidence: How sure are we?
  • Ease: How easy to implement?

Examples

Example 1: Complete AARRR Dashboard

Input:

Build an AARRR dashboard for a B2B SaaS product.
Monthly visitors: 50,000
Signups: 3,000
Activated users: 900
Paying customers: 150
Referrals generating signups: 45

Output:

## AARRR Dashboard: B2B SaaS

### Funnel Overview

┌──────────────────────────────────────────────────────────────┐ │ AARRR FUNNEL │ ├──────────────────────────────────────────────────────────────┤ │ │ │ ACQUISITION ACTIVATION RETENTION REVENUE REF │ │ 50,000 3,000 900 150 45 │ │ 100% 6% 30% 17% 30% │ │ │ │ ████████████ ████████ ████ ██ █ │ │ │ │ Visitors → Signups → Activated → Paid → Referral │ │ (-94%) (-70%) (-83%) (-70%) │ │ ⚠️ ⚠️ ✅ ⚠️ │ │ │ └──────────────────────────────────────────────────────────────┘


---

### Stage-by-Stage Analysis

#### 1. ACQUISITION

**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Monthly visitors | 50,000 | - | Baseline |
| Visitor → Signup | 6% | 2-5% | ✅ Good |
| CAC | $45 | <$50 | ✅ Good |
| Signups | 3,000 | - | - |

**Channel Breakdown:**
| Channel | Visitors | Signups | Conv % | CAC |
|---------|----------|---------|--------|-----|
| Organic | 20,000 | 1,500 | 7.5% | $0 |
| Paid Search | 15,000 | 900 | 6% | $60 |
| Content | 10,000 | 450 | 4.5% | $20 |
| Social | 5,000 | 150 | 3% | $80 |

**Analysis:** Acquisition is healthy. Organic performing best.
Paid social has high CAC—consider reallocating.

---

#### 2. ACTIVATION ⚠️ BOTTLENECK

**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Signup → Activated | 30% | 40-60% | ⚠️ Below |
| Time to activate | 3.2 days | <1 day | ⚠️ Slow |
| Activation rate | 900/3,000 | - | - |

**Activation Definition:**
"Activated" = Created first project + invited 1 team member

**Drop-off Analysis:**
| Step | Users | Drop-off |
|------|-------|----------|
| Signup complete | 3,000 | - |
| Email verified | 2,400 | -20% |
| Created project | 1,500 | -38% |
| Invited team | 900 | -40% ⚠️ |

**Primary Bottleneck:** "Invite team member" step losing 40%

**Experiment Ideas:**
| Experiment | Hypothesis | ICE |
|------------|------------|-----|
| Skip team invite in onboarding | Removes friction, activate solo first | 8/8/9 = 8.3 |
| In-app invite prompt (day 2) | Right timing, after value seen | 7/7/8 = 7.3 |
| Email team invite reminder | Low effort, catches drop-offs | 5/6/9 = 6.7 |

---

#### 3. RETENTION

**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Week 1 retention | 65% | 60%+ | ✅ Good |
| Month 1 retention | 45% | 40%+ | ✅ Good |
| DAU/MAU ratio | 28% | 20%+ | ✅ Good |
| Churn rate | 5%/month | <5% | ✅ OK |

**Retention Curve:**

Day 1: 100% ████████████████████ Day 7: 65% █████████████ Day 14: 52% ██████████ Day 30: 45% █████████ Day 60: 38% ████████ Day 90: 32% ██████


**Analysis:** Retention is solid. Users who activate tend to stick.
This confirms activation is the primary bottleneck.

---

#### 4. REVENUE

**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Activated → Paid | 17% | 5-15% | ✅ Strong |
| Trial → Paid | 17% | 10-25% | ✅ Good |
| ARPU | $89/mo | - | Baseline |
| LTV | $1,068 | - | 12mo × ARPU |
| LTV:CAC | 24:1 | >3:1 | ✅ Excellent |

**Revenue Breakdown:**
| Plan | Customers | % | MRR |
|------|-----------|---|-----|
| Starter ($29) | 60 | 40% | $1,740 |
| Pro ($89) | 75 | 50% | $6,675 |
| Enterprise ($249) | 15 | 10% | $3,735 |
| **Total** | **150** | - | **$12,150** |

**Analysis:** Strong conversion and healthy LTV:CAC.
Not a bottleneck—maintain current approach.

---

#### 5. REFERRAL ⚠️ OPPORTUNITY

**Metrics:**
| Metric | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Referral rate | 30% | 20%+ | ✅ Good |
| Viral coefficient | 0.45 | >1 = viral | ⚠️ Below |
| NPS | +32 | >30 | ✅ Good |
| Referral signups | 45 | - | - |

**Calculation:**
- 150 paying customers
- 30% refer (45 referrals)
- Each referral generates 1 signup
- Viral coefficient = 45/150 × (6% activation) = 0.018
- Not organic virality, but healthy referral base

**Experiment Ideas:**
| Experiment | Hypothesis | ICE |
|------------|------------|-----|
| Referral incentive (2-way) | Motivation for both parties | 7/8/7 = 7.3 |
| In-app share prompts | Right moment capture | 6/7/8 = 7.0 |
| Case study program | Social proof + referral | 5/6/6 = 5.7 |

---

### Priority Recommendations

| Priority | Focus | Current | Target | Impact |
|----------|-------|---------|--------|--------|
| 1 | Activation | 30% | 45% | +450 activated/mo |
| 2 | Referral | 30% | 50% | +30 referral signups |
| 3 | Paid channel mix | 6% conv | 8% conv | -$10 CAC |

**90-Day Goal:**
If activation improves 30% → 45%:
- +450 more activated users
- At 17% conversion = +76 new customers
- At $89 ARPU = +$6,800 MRR

---

### Dashboard Template

┌────────────────────────────────────────────────────────────┐ │ AARRR MONTHLY DASHBOARD January 2026 │ ├────────────────────────────────────────────────────────────┤ │ │ │ ACQUISITION ACTIVATION RETENTION │ │ Visitors: 50K Activated: 900 D30: 45% │ │ Signups: 3K Rate: 30% ⚠️ Churn: 5% │ │ CAC: $45 Time: 3.2d DAU/MAU: 28% │ │ │ │ REVENUE REFERRAL OVERALL │ │ Customers: 150 Referrals: 45 MRR: $12.2K │ │ Conv: 17% Rate: 30% LTV:CAC: 24:1 │ │ ARPU: $89 NPS: +32 Health: 🟢 │ │ │ │ BOTTLENECK: Activation (30% vs 45% target) │ │ EXPERIMENT: Skip team invite onboarding │ │ │ └────────────────────────────────────────────────────────────┘

Example 2: Quick Bottleneck Diagnosis

Input:

Diagnose this funnel:
- 100K visitors, 5K signups (5%)
- 4K activated (80%)
- 1K retained at D30 (25%)
- 200 paid (20%)

Output:

## Quick AARRR Diagnosis

### Funnel

| Stage | Users | Conv | Benchmark | Status |
|-------|-------|------|-----------|--------|
| Acquisition | 100K → 5K | 5% | 2-5% | ✅ Good |
| Activation | 5K → 4K | 80% | 40-60% | ✅ Excellent |
| Retention | 4K → 1K | 25% | 40%+ | ❌ Problem |
| Revenue | 1K → 200 | 20% | 5-15% | ✅ Strong |

### Bottleneck: RETENTION

**Problem:** Only 25% retained at D30 (should be 40%+)

**What this means:**
- Great at attracting and activating
- Users try it, find value initially
- But don't form a habit / come back
- Losing 3,000 activated users monthly

**Likely Causes:**
1. Single-use case (solved problem, left)
2. Not enough ongoing value
3. Poor re-engagement
4. Competitor switching

**Recommended Experiments:**
1. User interviews with churned users
2. Email re-engagement sequence
3. Weekly value summary email
4. Add recurring use case

**Impact if fixed:**
If retention → 40%: 1,600 retained → 320 paid
That's +120 customers/month (+60%)

Skill Boundaries

What This Skill Does Well

  • Structuring growth metrics
  • Identifying funnel bottlenecks
  • Prioritizing experiments
  • Creating dashboards

What This Skill Cannot Do

  • Access your actual data
  • Know your specific definitions
  • Run experiments
  • Guarantee results

Iteration Guide

Follow-up Prompts:

  • "Design activation experiments for [problem]"
  • "What metrics matter for [stage]?"
  • "Create a retention analysis framework"
  • "How do we improve [specific conversion]?"

References

  • Dave McClure - Pirate Metrics (500 Startups)
  • Reforge Growth Series
  • Amplitude Product Analytics
  • Mixpanel Growth Framework

Related Skills

  • product-led-growth - PLG motions
  • growth-loops - Sustainable growth
  • startup-metrics - Investor metrics

Skill Metadata

  • Domain: Growth
  • Complexity: Intermediate
  • Mode: cyborg
  • Time to Value: 2-3 hours for full setup
  • Prerequisites: Analytics access, metric definitions

Frequently asked questions

What does the Aarrr Metrics AI skill do?

Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis

Why use Aarrr Metrics on TypingMind?

Because you install it once and use it with any model. Aarrr Metrics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aarrr Metrics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVlabs/Skill2Env/tree/main/SkillHub/skills/aarrr-metrics. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aarrr Metrics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aarrr Metrics?

As many as you like. As long as a model supports skills, you can use Aarrr Metrics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aarrr Metrics AI skill free?

Yes. It is published on GitHub by NVlabs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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