google-search-console
When the user wants to analyze Google Search Console data, use the GSC API, or interpret search performance. Also use when the user mentions "GSC," "Search…
When the user wants to plan, implement, or optimize referral program strategy. Also use when the user mentions "referral program," "referral marketing," "user referral," "refer-a-friend," "word-of-mouth growth," "referral rewards," "referral tracking," "referral code," "referral
$ npx -y skills add kostja94/marketing-skills --skill referral-program --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/referral-programContext preview
The summary Claude sees to decide when to auto-load this skill.
When the user wants to plan, implement, or optimize referral program strategy. Also use when the user mentions "referral program," "referral marketing," "user referral," "refer-a-friend," "word-of-mouth growth," "referral rewards," "referral tracking," "referral code," "referral
name: referral-program description: When the user wants to plan, implement, or optimize referral program strategy. Also use when the user mentions "referral program," "referral marketing," "user referral," "refer-a-friend," "word-of-mouth growth," "referral rewards," "referral tracking," "referral code," "referral incentives," or "viral loop." For referral landing copy, use landing-page-generator. metadata: version: 1.0.1
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.
**Check for project context first:** If `.claude/project-context.md` or `.cursor/project-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
| Dimension | Referral | Affiliate | Influencer | |-----------|----------|-----------|------------| | **Who** | Existing users | Professional promoters | KOLs | | **Incentive** | Discounts, credits | Commission | Fees, product | | **Barrier** | Low (all users) | Medium | High | | **Conversion** | 3%-5% | Varies | Varies |
**Referral vs affiliate**: Referral needs no landing page or application; integrated in dashboard. Affiliate requires landing page and approval.
| Model | Use | |-------|-----| | **Two-way** | Both referrer and referee get rewards; highest participation | | **One-way** | Only referrer rewarded; cost control | | **Tiered** | Rewards 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.
| Type | Use | |------|-----| | **Link-based** | Unique referral link; easy to implement; accurate tracking; share via email, social, SMS; works for web and app | | **Code-based** | Referral code (e.g. FRIEND20); memorable; offline events; mobile-friendly input | | **Social referral** | Share buttons (Facebook, X, LinkedIn); viral spread; friend trust; young users |
| Method | Use | |-------|-----| | **Cookie** | Web apps; 30-90 day window | | **URL params** | All platforms; persistent in link | | **Referral code** | Mobile, offline; manual entry | | **Account association** | Long-term tracking; subscription products |
**Attribution window**: 30-90 days typical; 180 days for subscription. First-touch attribution to avoid double-counting.
| Risk | Action | |------|--------| | **Self-referral** | Detect same device, payment, IP | | **Fake accounts** | Validate email, payment; monitor patterns | | **Bulk/automation** | Rate limits; anomaly detection | | **Per-user cap** | e.g. Max 10 referrals per user |
Use tool anti-fraud features; audit referrals regularly.
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
| Approach | Use | |----------|-----| | **Self-build** | Full control; low cost; URL params or cookie + reward logic + fraud checks; open-source (e.g. RefRef) for faster start | | **Third-party** | Fast 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.
| Tool | Use | |------|-----| | **Cello** | SaaS; AI-driven automation | | **Viral Loops** | Referral + waitlist + contests | | **ReferralCandy** | Shopify, e-commerce | | **Impact** | Enterprise; unified platform | | **RefRef** | Open-source; self-hosted |
Referral rate, conversion, CAC, LTV of referred users, referred-user retention.
Markdown skill library for AI agents - SEO, content, pages, paid ads, channels, strategies. Add project context + skills; your agent delivers tailored, production-ready output. Works with Cursor, Claude Code, OpenClaw, Lovable, and any AI that reads markdown.
Repo: kostja94/marketing-skills
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