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Agent Skill · 开发

referral-program(推荐计划)

referral-program 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

referral-program 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 9,745。

总安装 2,568周安装 107GitHub Stars 406下载 856核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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/openclaudia/openclaudia-skills
仓库路径:skills/referral-program
安装命令:
npx skills add https://github.com/openclaudia/openclaudia-skills --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/openclaudia/openclaudia-skills --skill referral-program

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

referral-program 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。适用于开发类任务,支持代码协作与 Issue 跟踪管理。

通过 npx skills add 命令从指定 GitHub 仓库安装,具体用法需结合原始 README 进一步确认。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操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/referral-program

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/openclaudia/openclaudia-skills --skill referra…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

referral-program 怎么安装?

在终端执行 npx skills add https://github.com/openclaudia/openclaudia-skills --skill referral-program。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

referral-program 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

referral-program 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

referral-program 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

referral-program 用的人多吗?

公开数据是累计安装 2,568 次,近一周安装 107 次,下载 856 次,GitHub 406 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

referral-program 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
referral-program

推荐计划 · 本页

2,568406需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Referral Program Design

Design effective referral programs and viral loops that drive sustainable growth.


1. Referral Program Frameworks

One-Sided Incentives

Only the referrer gets rewarded.

Best for:

  • Products with strong organic word-of-mouth
  • Low-friction signups where the referred user needs no extra motivation
  • Cost-sensitive businesses

Examples:

  • Uber: "$10 credit for every friend you refer"
  • Amazon Associates: Commission on referred purchases

Template:

Refer a friend and get [reward].
Share your unique link: [referral_url]

Two-Sided Incentives

Both referrer and referred user get rewarded.

Best for:

  • Products requiring activation effort from new users
  • Competitive markets where new users need a nudge
  • Subscription businesses

Examples:

  • Dropbox: Both get 500MB extra storage
  • Airbnb: Referrer gets $25 credit, friend gets $40 off first stay
  • PayPal: Both get $10 when friend makes first transaction

Template:

Give [friend_reward], get [referrer_reward].
Share your link and you both win: [referral_url]

Tiered Incentives

Rewards increase with number of successful referrals.

Example tier structure:

ReferralsReward
1Free month
3Exclusive feature unlock
5Premium plan for 3 months
10Lifetime premium access
25Cash payout or swag box

Best for:

  • Creating power referrers / ambassadors
  • Products with passionate user bases
  • Building a referral leaderboard culture

2. Viral Coefficient Calculation

The viral coefficient (K-factor) determines whether your referral loop is self-sustaining.

Formula

K = i * c

Where:
  i = number of invites sent per user
  c = conversion rate of each invite

If K > 1: viral growth (each user brings more than one new user)
If K < 1: referrals supplement but don't replace other acquisition

Example Calculation

Users send an average of 5 invites (i = 5)
15% of invites convert to signups (c = 0.15)
K = 5 * 0.15 = 0.75

With 1,000 initial users:
- Cycle 1: 1,000 * 0.75 = 750 new users
- Cycle 2: 750 * 0.75 = 563 new users
- Cycle 3: 563 * 0.75 = 422 new users
- Total after 10 cycles: ~3,570 additional users from referrals

Viral Cycle Time

The speed of the viral loop matters as much as K:

Effective growth = K / cycle_time

A K of 0.5 with a 1-day cycle > K of 0.8 with a 30-day cycle

How to Improve K

LeverAction
Increase invites (i)Make sharing frictionless, prompt at key moments
Increase conversion (c)Better landing page, stronger incentive, social proof
Reduce cycle timeInstant reward delivery, real-time notifications

3. Referral Channels

Email Referral

Pros: High conversion, personal, trackable Cons: Lower volume, requires email access

Template:

Subject: I thought you'd like [Product] -- here's [reward] to try it

Hey [Name],

I've been using [Product] for [time] and it's been great for [specific benefit].

I wanted to share my referral link so you can get [friend_reward]:
[referral_url]

[Your name]

Social Media Sharing

Pros: High reach, low effort, viral potential Cons: Lower conversion rate, less personal

Platform-specific templates:

Twitter/X:

I just [achievement/milestone] with @Product! If you want to try it,
use my link and we both get [reward]: [referral_url]

LinkedIn:

I've been using [Product] to [professional benefit] and the results
have been impressive: [specific metric].

If you're looking for [solution], here's my referral link
(we both get [reward]): [referral_url]

WhatsApp/SMS:

Hey! I've been using [Product] and really like it. They have a
referral deal -- we both get [reward] if you sign up through my link:
[referral_url]

In-App Referral

Pros: Highest intent, contextual, frictionless Cons: Only reaches existing users

Best practices:

  • Show referral prompt after a success moment (completed task, achievement, positive outcome)
  • Pre-populate sharing message
  • Show referral progress and rewards earned
  • Add referral widget to account/settings page

Unique Link vs. Referral Code

MethodProsCons
Unique linkFrictionless, works in any channelHarder to share verbally
Referral codeEasy to remember, shareable verballyExtra step at signup
BothMaximum flexibilityMore complex to implement

4. Referral Landing Page Template

The page a referred user sees when they click the referral link.

Structure

[Hero Section]
- Headline: "[Referrer's name] invited you to [Product]"
- Subheadline: "Sign up now and get [friend_reward]"
- CTA button: "Claim your [reward]"

[Social Proof]
- "[Referrer's name] and X others use [Product]"
- Logos of known customers
- Key metric: "Trusted by X users"

[Value Proposition]
- 3 key benefits with icons
- Brief product description

[How It Works]
- Step 1: Sign up (takes 30 seconds)
- Step 2: [Key activation action]
- Step 3: Enjoy [reward] + the product

[CTA Repeat]
- "Join [Referrer's name] on [Product]"
- Urgency: "This offer expires in [X days]"

[FAQ]
- When do I get my reward?
- What does [Product] do?
- Is there a catch?

5. Case Studies

Dropbox (2008-2010)

  • Mechanic: Two-sided -- both get 500MB extra storage
  • Result: 3,900% growth in 15 months (100K to 4M users)
  • Key insight: The reward (storage) was the product itself, making it self-reinforcing
  • Viral coefficient: Estimated ~0.6-0.7 (supplemental, not purely viral)

Airbnb (2014)

  • Mechanic: Two-sided -- $25 travel credit for referrer, $40 off first booking for friend
  • Result: 300% increase in bookings from referral program 2.0
  • Key insight: Redesigned referral page to feel like a gift, not spam. Personalized with referrer's photo and travel history
  • Viral coefficient: Low K but high LTV per referred user

PayPal (1999-2000)

  • Mechanic: Two-sided -- $20 per referral (later reduced to $10, then $5)
  • Result: 7-10% daily growth, reaching 1M users in first year
  • Key insight: Cash incentive drove massive initial growth, then reduced as network effects kicked in
  • Cost: $60-70M in referral bonuses, but each user was worth much more in LTV

Robinhood (2014-2015)

  • Mechanic: Wait-list with move-up-the-line incentive + free stock for referrals
  • Result: 1M waitlist signups before launch
  • Key insight: Scarcity (waitlist) + social proof (your position) + tangible reward (free stock)

6. Implementation Checklist

Technical Setup

  • Generate unique referral links/codes per user
  • Build referral tracking (link clicks, signups, activations, rewards)
  • Set up attribution window (how long after click does signup count?)
  • Implement fraud detection (self-referral, fake accounts, VPN abuse)
  • Build reward fulfillment (automatic credit, manual approval, or hybrid)
  • Create referral dashboard for users (invites sent, pending, completed, rewards earned)

Fraud Prevention

  • Block self-referrals (same IP, same device, same email domain)
  • Require activation action before reward (not just signup)
  • Set daily/weekly referral limits per user
  • Flag suspicious patterns (bulk signups from same IP range)
  • Implement clawback for fraudulent referrals

Program Design Decisions

DecisionOptions
Reward typeCash, credit, product features, swag, charity donation
Reward timingOn signup, on activation, on first purchase
Reward amountTest multiple amounts; higher is not always better
ExpirationReferral links expire after X days? Rewards expire?
LimitMax referrals per user per month?
EligibilityAll users or only paid/active users?

7. Referral Email Sequences

Invite Reminder (to existing users)

Subject: You have [X] invites waiting

Hey [Name],

Did you know you can give your friends [reward] and get [reward] for yourself?

You've invited [0/N] friends so far. Share your link:
[referral_url]

Here's what [Product] users are saying:
"[Testimonial]" -- [Customer name]

Referred User Welcome

Subject: [Referrer] sent you a gift -- [reward] inside

Hey there!

[Referrer's name] thinks you'd love [Product], so they're giving you [reward] to try it.

[CTA: Claim your reward]

What is [Product]?
[1-sentence description]

What you'll get:
- [Benefit 1]
- [Benefit 2]
- [Benefit 3]
- Plus [friend_reward] from [Referrer's name]

This offer expires in [X] days.

Referral Success Notification

Subject: [Friend's name] just signed up -- you earned [reward]!

Great news, [Name]!

[Friend's name] accepted your invitation and signed up for [Product].
Your [reward] has been added to your account.

Keep sharing: you've referred [X] friends so far.
[referral_url]

[Show progress toward next tier if using tiered rewards]

8. Measuring Success

Key Metrics

MetricFormulaGood Benchmark
Participation rateUsers who share / total users15-25%
Shares per userTotal shares / participating users3-5
Click-through rateLink clicks / shares20-40%
Conversion rateSignups / link clicks10-25%
Viral coefficient (K)Invites * conversion rate0.3-0.7 typical
Viral cycle timeAvg days from invite to new user's first invite1-7 days
Referral revenueRevenue from referred usersTrack separately
CAC via referralReward cost / acquired usersCompare to paid CAC

A/B Tests to Run

  1. Reward amount ($10 vs $20 vs $50)
  2. Reward type (credit vs cash vs feature unlock)
  3. One-sided vs two-sided incentive
  4. Referral prompt timing (after signup vs after first success)
  5. Landing page copy (gift framing vs deal framing)
  6. Email subject lines for referral invites
  7. Social sharing copy variations

平台分布

Codex

38.21%
按下载量换算327

Claude

27.1%
按下载量换算232

Cursor

20.26%
按下载量换算173

Gemini CLI

9.39%
按下载量换算80

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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