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aarrr-metricsaarrr 指标

Agent Skill

aarrr-metrics 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

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unknown

最后核验

2026-05-01

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来源可访问

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复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:aarrr-metrics(aarrr 指标)
来源仓库:https://github.com/guia-matthieu/clawfu-skills
仓库路径:skills/aarrr-metrics
安装命令:
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill aarrr-metrics
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

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skills.shnpx skills
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill aarrr-metrics

简介

aarrr-metrics 基于 AARRR 海盗指标框架,用于分析和优化产品增长漏斗。

  • 适用于构建增长看板、识别转化瓶颈和制定实验优先级等场景。
  • 通过定义各阶段关键指标、分析漏斗转化率和建议实验方向来支持决策。
  • 安装需通过 npx 从指定 GitHub 仓库添加,使用前请确认环境权限与依赖配置。
  • 注意该技能依赖外部数据源,需确保网络连通性及目标平台对 MCP 工具的支持状态。

SKILL.md

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

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