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referral-program推荐计划

Agent Skill

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

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安装说明

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

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

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:referral-program(推荐计划)
来源仓库:https://github.com/majesticlabs-dev/majestic-marketplace
仓库路径:skills/referral-program
安装命令:
npx skills add https://github.com/majesticlabs-dev/majestic-marketplace --skill referral-program
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。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/majesticlabs-dev/majestic-marketplace --skill referral-program

简介

用于辅助测试设计、自动化测试和用例整理,支持回归验证。

  • 适合编写单元测试、端到端测试或根据失败日志定位问题。referral-program 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 使用时应确认项目测试框架、运行命令和夹具数据,避免误改逻辑。
  • 涉及浏览器或外部服务时,需区分本地模拟、测试环境与生产环境。
  • 安装方式:通过 npx 从 GitHub 仓库添加技能。

SKILL.md

Referral Program Architect

Audience: Growth teams and founders designing customer acquisition loops through referrals.

Goal: Design a complete referral program—incentive structure, sharing mechanics, tracking system, and ROI projections—grounded in viral coefficient math and behavioral psychology.

Conversation Starter

Use AskUserQuestion to gather initial context. Begin by asking:

"I'll help you design a referral program that turns your customers into your best acquisition channel.

Please provide:

  1. Business Model: What do you sell? (SaaS, e-commerce, marketplace, service)
  2. Pricing: What's your price point? (affects incentive structure)
  3. Current Acquisition Cost: What do you spend to acquire a customer now?
  4. Customer Profile: Who are your customers? What motivates them?
  5. Product Type: Is this something people naturally talk about? Why/why not?
  6. Existing Word-of-Mouth: Do customers already refer? What's happening organically?

I'll research successful referral programs in your space and design a complete program architecture."

Research Methodology

Use WebSearch extensively to find:

  • Referral program case studies (Dropbox, Airbnb, PayPal, Uber)
  • Industry-specific referral benchmarks
  • Viral coefficient calculations and optimization
  • Incentive effectiveness research
  • Legal considerations for referral rewards

Required Deliverables

1. Program Structure Design

TypeBest For
Double-sidedMost businesses (both parties motivated)
Single-sided (referrer)High-margin businesses
Single-sided (referee)Competitive markets
TieredGamification focus

Reward Options:

Reward TypeBest For
Cash/creditE-commerce, marketplaces
Product discountSubscription, SaaS
Free monthsSaaS with high retention
Premium featuresFreemium models
Exclusive accessPremium brands

2. Incentive Economics

Current CAC: $[X]
Referral Reward Cost: $[Y]
If conversion rate is [Z]%, effective CAC = $[Y ÷ Z]

Break-even conversion rate: [Y ÷ X]%
Target conversion rate: [Above break-even]%

ROI Projection Table:

ScenarioReferrals/MonthConversionsCostLTV GeneratedROI
Conservative[X][Y]$[Z]$[A][B]%
Expected[X][Y]$[Z]$[A][B]%
Optimistic[X][Y]$[Z]$[A][B]%

3. Sharing Mechanics

Link Format: yoursite.com/r/[UNIQUE_CODE]

Sharing Channels:

ChannelFriction LevelExpected Volume
Direct link copyVery lowHigh
Email inviteLowMedium
Social shareLowMedium
Messenger/WhatsAppLowHigh (mobile)
QR codeMediumLow but high-intent

Share Prompt Placement:

LocationTrigger
Post-purchaseOrder confirmation
DashboardEvery login (subtle)
Post-successAfter achieving goal
Email footerEvery transactional email
In-app promptAfter [X] days as customer

4. Messaging Templates

Full templates for:

  • Email invite (referrer to friend)
  • Landing page (referee arrives)
  • Social share copy (Twitter, LinkedIn, Facebook)
  • Thank you messages (referrer and referee)

See assets/messaging-templates.yaml

5. Viral Coefficient Framework

K = i × c

Where:

  • i = invitations sent per customer
  • c = conversion rate of invitations
K-FactorMeaning
< 0.5Weak referrals, needs other channels
0.5-1.0Healthy referrals, amplifies growth
> 1.0Viral growth, self-sustaining

To improve:

  • Increase invitations (i): More prompts, easier sharing, gamification
  • Increase conversion (c): Better landing page, higher incentive, trust signals

6. Tracking & Attribution

  • Attribution requirements
  • Implementation options (URL params, unique links, hybrid)
  • Fraud prevention measures
  • Attribution window recommendations

See assets/tracking-launch.yaml

7. Launch Plan

Phase 1: Soft Launch (Week 1-2)

  • Top 10% customers (NPS promoters)
  • Personal outreach
  • Monitor for issues

Phase 2: Expansion (Week 3-4)

  • All customers
  • In-app prompts
  • Email announcement

Phase 3: Optimization (Week 5+)

  • A/B test incentives
  • Add gamification
  • Scale sustainably

Full roadmap: assets/tracking-launch.yaml

Output Format

# REFERRAL PROGRAM BLUEPRINT: [Business Name]

## Executive Summary
[Strategy and expected impact]

## Program Structure
[Incentive design and mechanics]

## Economics Model
[CAC comparison, ROI projection]

## Sharing System
[Links, channels, placements]

## Messaging Library
[All templates and copy]

## Viral Coefficient
[K-factor analysis and optimization]

## Tracking System
[Attribution and fraud prevention]

## Launch Plan
[Phased rollout with milestones]

## Quick Start Checklist
[ ] Finalize incentive structure
[ ] Set up tracking/attribution
[ ] Create referral landing page
[ ] Build sharing mechanics
[ ] Write email templates
[ ] Soft launch to advocates
[ ] Monitor and optimize

Quality Standards

  • Research case studies: Reference successful programs
  • Economics-driven: Every recommendation tied to CAC/LTV math
  • Copy-ready: Provide usable templates
  • Fraud-aware: Include prevention measures
  • Measurable: Clear metrics at every stage

Tone

Strategic and growth-focused. Write like a Head of Growth presenting a viral strategy to the CEO—clear economics, proven tactics, and realistic projections.

适合场景

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02

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能力概览

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能力 2

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能力 3

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能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.4%
按下载量换算108

Claude

26.95%
按下载量换算80

Cursor

18.56%
按下载量换算55

Gemini CLI

8.11%
按下载量换算24

安全审计

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可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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