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channels-referral渠道推荐

Agent Skill

channels-referral 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。

总安装

212

周安装

9

GitHub Stars

409

下载量

74
CodexClaudeCursorGemini CLI

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:channels-referral(渠道推荐)
来源仓库:https://github.com/kostja94/marketing-skills
仓库路径:skills/channels-referral
安装命令:
npx skills add https://github.com/kostja94/marketing-skills --skill channels-referral
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/kostja94/marketing-skills --skill channels-referral

简介

channels-referral 用于查找、检索和筛选与推荐计划策略相关的信息,适合在 Codex、Claude、Cursor、Gemini CLI 中获取增长策略支持。

  • 适用于需要为 AI/SaaS 产品制定推荐计划的场景,提供转化率与获客成本对比数据参考。
  • 使用时可结合安装命令和原始 README 继续核验具体用法,首次调用可简要介绍其价值。
  • 安装前建议确认权限范围和维护状态,以及是否会触发联网或文件读写操作。
  • 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。

SKILL.md

Channels: Referral

Guides referral program strategy for AI/SaaS products. Leverage existing users to drive growth; 3%-5% conversion vs 1%-2% for ads; CAC 50%-70% lower; referred users LTV 30%-50% higher, retention 20%-30% higher. Referral is necessity in overseas markets, not alternative.

When invoking: On first use, if helpful, open with 1-2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.

Initial Assessment

Check for product marketing context first: If .claude/product-marketing-context.md or .cursor/product-marketing-context.md exists, read it for product, audience, and value proposition.

Identify:

  1. Product type: SaaS, AI tool, subscription
  2. User base: Size, engagement, retention
  3. Goal: Signups, purchases, or both

Referral vs. Affiliate vs. Influencer

DimensionReferralAffiliateInfluencer
WhoExisting usersProfessional promotersKOLs
IncentiveDiscounts, creditsCommissionFees, product
BarrierLow (all users)MediumHigh
Conversion3%-5%VariesVaries

Referral vs affiliate: Referral needs no landing page or application; integrated in dashboard. Affiliate requires landing page and approval.

Reward Models

ModelUse
Two-wayBoth referrer and referee get rewards; highest participation
One-wayOnly referrer rewarded; cost control
TieredRewards increase with referral count (e.g. $10 for 1-5, $15 for 6-10, $20 for 11+); incentivizes volume

Benchmark: Rewards typically 10%-30% of product price; ~11% off or ~$21 value; weak incentives = low participation. Triggers: signup, purchase, activation, or sustained use.

Mechanism Types

TypeUse
Link-basedUnique referral link; easy to implement; accurate tracking; share via email, social, SMS; works for web and app
Code-basedReferral code (e.g. FRIEND20); memorable; offline events; mobile-friendly input
Social referralShare buttons (Facebook, X, LinkedIn); viral spread; friend trust; young users

Tracking & Attribution

MethodUse
CookieWeb apps; 30-90 day window
URL paramsAll platforms; persistent in link
Referral codeMobile, offline; manual entry
Account associationLong-term tracking; subscription products

Attribution window: 30-90 days typical; 180 days for subscription. First-touch attribution to avoid double-counting.

Fraud Prevention

RiskAction
Self-referralDetect same device, payment, IP
Fake accountsValidate email, payment; monitor patterns
Bulk/automationRate limits; anomaly detection
Per-user cape.g. Max 10 referrals per user

Use tool anti-fraud features; audit referrals regularly.

Design Framework

  1. Reward structure: Type (cash, discount, credits, free service); amount (10%-30% of price); trigger; cap
  2. Tracking: Choose method; set attribution window; first-touch rule
  3. UX: One-click share; clear rules; dashboard with referral data; notify on success
  4. Fraud prevention: See above
  5. Monitor & optimize: Referral rate, conversion, CAC, LTV; A/B test rewards and flow

Best Practices

  • Run multiple programs: Target different audiences, stages, goals
  • Tiered rewards: Motivate top performers; progressive incentives
  • Friction-free sharing: Mobile-friendly; one-click share
  • Time-boxed incentives: "Refer this week for $15 off" creates urgency
  • Placement: Web, email, app, in-product touchpoints; dashboard integration primary

Implementation

ApproachUse
Self-buildFull control; low cost; URL params or cookie + reward logic + fraud checks; open-source (e.g. RefRef) for faster start
Third-partyFast launch; Cello, Viral Loops, ReferralCandy (e-commerce), Impact (enterprise); monthly fee

Placement: Most programs integrate in product dashboard; no landing page or application needed. Optional landing page for value prop, rewards, and case studies.

Startup cost: Typically hundreds for tools + dev.

Tools

ToolUse
CelloSaaS; AI-driven automation
Viral LoopsReferral + waitlist + contests
ReferralCandyShopify, e-commerce
ImpactEnterprise; unified platform
RefRefOpen-source; self-hosted

KPIs

Referral rate, conversion, CAC, LTV of referred users, referred-user retention.

Output Format

  • Reward model and mechanism type (link/code/social)
  • Tracking approach and attribution window
  • Placement (dashboard vs landing page)
  • Fraud prevention measures
  • Tool selection (self-build vs third-party)
  • KPI framework

Related Skills

  • channels-affiliate: Different audience; can run both
  • channels-influencer: Brand building vs. user-driven growth
  • channels-directories: Directory submission for discovery; referral for user-driven growth
  • analytics-tracking: Referral link tracking, UTM

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

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

36.66%
按下载量换算27

Claude

31.78%
按下载量换算24

Cursor

17.99%
按下载量换算13

Gemini CLI

8.37%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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