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Agent skill

Referral Program

by borghei in borghei/Claude-Skills

Referral and affiliate program design covering referral loop architecture, incentive design, trigger moment optimization, viral coefficient modeling, affiliate program structure, and optimization…

MITAuto-check passedMarketing & SEO

Install Referral Program

skills CLI
$ npx skills add borghei/Claude-Skills --skill referral-program -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install borghei/Claude-Skills referral-program --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/business-growth/referral-program .claude/skills/referral-program && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
referral-program
GitHub stars
874
Token cost
~1.8k tokens
SKILL.md length
802 words
Files
8 (incl. scripts, references)
Skills in repo
364
Repo updated
First seen
Licence
MIT

At a glance

Referral and affiliate program design covering referral loop architecture, incentive design, trigger moment optimization, viral coefficient modeling, affiliate program structure, and optimization…

  • Works in 5 steps: Pick the program type — use the Referral… → Build the loop in order — trigger →… → Size the incentive — cap reward at <30%… → …
  • Moment optimization
  • SKILL.md covers Core Capabilities, When to Use, Clarify First and Quick Start, plus 3 more sections
  • Runs Python scripts from its folder

What it does

Referral Program is an agent skill from borghei/Claude-Skills. Referral and affiliate program design covering referral loop architecture, incentive design, trigger moment optimization, viral coefficient modeling, affiliate program structure, and optimization playbook.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/loop-and-incentives.md`, `references/modeling-and-affiliate.md` and `references/optimization-and-operations.md`).

It sits in Marketing & SEO, covering Referral and retention marketing. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.

When your agent uses it

  • Moment optimization
  • Viral coefficient modeling
  • Affiliate program structure
  • Optimization playbook

Example prompts

  • “/referral-program”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Pick the program type — use the Referral vs Affiliate Decision table (enthusiastic/social customers → referral; team buyers → affiliate).
  2. Build the loop in order — trigger → share → convert → reward; a broken Stage 1 can't be fixed by a bigger reward at Stage 4.
  3. Size the incentive — cap reward at <30% of first payment; go double-sided if referral rate <1%.
  4. Model and validate — run the scripts (referral_economics_calculator.py, referral_funnel_analyzer.py, affiliate_commission_modeler.py) to…
  5. Optimize by priority — fix awareness first, then share flow, then referred experience, then the incentive.

What it can do on your machine

Read from SKILL.md and the folder at commit c9a1487. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Referral Program loads about 1.8k tokens when it runs, and up to ~7.6k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 802 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~56
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from borghei/Claude-Skills at commit c9a1487, republished under its MIT licence (© borghei). 802 words, ~1,770 tokens.

Download SKILL.mdSave it as .claude/skills/referral-program/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
referral-program
description
Referral and affiliate program design covering referral loop architecture, incentive design, trigger moment optimization, viral coefficient modeling, affiliate program structure, and optimization playbook.
license
MIT + Commons Clause
metadata.version
1.1.0
metadata.author
borghei
metadata.category
business-growth
metadata.updated
2026-06-15
metadata.tags
referral, affiliate, growth, viral, word-of-mouth, acquisition

Referral Program

Production-grade referral and affiliate program framework covering the 4-stage referral loop, incentive design methodology, trigger moment optimization, share mechanics, viral coefficient modeling, affiliate program architecture, and systematic optimization playbook. Designed to build programs that compound, not collect dust.

Core Capabilities

  • Program type & loop design — referral vs affiliate decision, plus the 4-stage loop (trigger → share → convert → reward)
  • Incentive design — single- vs double-sided, reward types, tiered gamification, reward economics against LTV/CAC
  • Trigger & share mechanics — in-product and email trigger points, share channel priority, first-person share copy
  • Referred-user experience — referral landing page, attribution rules, program copy set (prompts, emails, dashboards)
  • Growth math — K-factor modeling, revenue impact models, and lever-by-lever K improvement
  • Affiliate framework — commission models, tier systems, partner toolkit, recruitment
  • Optimization — diagnose-before-optimize playbook, metric benchmarks, troubleshooting, and three Python tools

When to Use

  • The user asks to "design a referral program", "launch an affiliate program", or "improve viral growth"
  • The decision between customer referral vs affiliate program needs to be made
  • An existing referral program has stalled (K-factor <1, low share rate, low referred-user conversion)
  • Reward structure needs sizing against CAC, margin, or LTV
  • Trigger moments need to be identified (when to ask, which in-product events, which lifecycle emails)
  • The user says "word-of-mouth isn't working" or "we want to add a refer-a-friend flow"

Clarify First

Before designing the referral program, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • Program type — customer referral vs affiliate (enthusiastic/social customers vs team buyers) (selects the entire framework)
  • Trigger moment — the in-product or lifecycle point where you ask (a broken Stage 1 can't be fixed by a bigger reward at Stage 4)
  • Reward economics — first-payment value, margin, and CAC (caps the reward at <30% of first payment)
  • Current referral rate (if any) — decides single- vs double-sided incentive and which stage to fix first

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the deliverable.

Quick Start

  1. Pick the program type — use the Referral vs Affiliate Decision table (enthusiastic/social customers → referral; team buyers → affiliate).
  2. Build the loop in order — trigger → share → convert → reward; a broken Stage 1 can't be fixed by a bigger reward at Stage 4.
  3. Size the incentive — cap reward at <30% of first payment; go double-sided if referral rate <1%.
  4. Model and validate — run the scripts (referral_economics_calculator.py, referral_funnel_analyzer.py, affiliate_commission_modeler.py) to size rewards, find the weakest stage, and model affiliate tiers.
  5. Optimize by priority — fix awareness first, then share flow, then referred experience, then the incentive.
Show full SKILL.md (374 more words)Show less

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

  • references/loop-and-incentives.md — Referral vs Affiliate decision table, the full 4-stage loop with per-stage tables, incentive design (single/double-sided, reward types, tiers, economics), and trigger moment architecture. Read when designing the core program.
  • references/share-and-experience.md — share channel priority, share message templates, referral landing page layout, attribution rules, and the program copy set (in-app prompt, dashboard, post-activation email). Read when building the sharing flow and referred-user experience.
  • references/modeling-and-affiliate.md — K-factor calculation and improvement levers, plus the full affiliate framework (commission structure, tier system, toolkit, recruitment). Read when modeling growth math or designing an affiliate program.
  • references/optimization-and-operations.md — optimization playbook, key metrics and benchmarks, revenue impact model, output artifacts, full tool reference, troubleshooting table, success criteria, and anti-patterns. Read when diagnosing a stalled program or operating the scripts.

Scope & Limitations

In scope: Customer referral program design (4-stage loop), incentive structure (single-sided, double-sided, tiered), trigger moment architecture, share mechanics, referral landing page specifications, viral coefficient modeling, affiliate program framework (commission models, tier systems, recruitment), and systematic optimization playbook.

Out of scope: Referral landing page visual design and CRO (use page-cro), signup flow optimization for referred users (use signup-flow-cro), post-signup onboarding for referred users (use onboarding-cro), churn prevention for referred customers (use churn-prevention), and reward pricing alignment (use pricing-strategy). Scripts operate on local data only -- no integrations with referral platforms (ReferralHero, Viral Loops, PartnerStack, etc.).

Limitations: K-factor benchmarks assume consumer or prosumer SaaS; B2B enterprise referral programs have different dynamics (lower K but higher per-referral value). Affiliate commission benchmarks (20-30% recurring) are SaaS-specific; marketplace and e-commerce commissions follow different models. Attribution windows (30-90 day cookies) face increasing limitations from browser privacy features (Safari ITP, Chrome third-party cookie deprecation). Revenue projections are estimates based on provided conversion rates.

Integration Points

  • pricing-strategy -- Referral reward sizing must align with pricing margins and LTV; reward should be <30% of first payment
  • signup-flow-cro -- Referred user signup flow should pre-fill email, show referrer context, and minimize friction
  • onboarding-cro -- Referred users may need different onboarding path (they arrive with context from the referrer)
  • churn-prevention -- Monitor referred customer retention separately; high referral churn wastes acquisition spend
  • page-cro -- Referral landing page conversion optimization follows page-cro methodology
  • popup-cro -- Post-purchase or post-milestone popups are natural referral trigger points

© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in business-growth/referral-program of borghei/Claude-Skills.

  • SKILL.md
  • references/loop-and-incentives.md
  • references/modeling-and-affiliate.md
  • references/optimization-and-operations.md
  • references/share-and-experience.md
  • scripts/affiliate_commission_modeler.py
  • scripts/referral_economics_calculator.py
  • scripts/referral_funnel_analyzer.py

Open the folder on GitHubat commit c9a1487

Compare with similar skills

Referral Program next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Referral Program compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Referral Program this skillborghei/Claude-Skills874—~1.8kAutomated safety check: PassMIT
Referral Programfreekmurze/dotfiles1k16 repos~1.8kAutomated safety check: PassNone
Churn Preventionrongxinzy/RongxinAI1543 repos~2.6kAutomated safety check: PassMIT
100m Leadsgetagentseal/founder-playbook721—~2.3kAutomated safety check: PassMIT
Churn Preventionfreekmurze/dotfiles1k12 repos~4.4kAutomated safety check: PassNone
ReferralsCesarjoquin/Marketing-Skills1991 repos~1.9kAutomated safety check: PassMIT

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Categories

Questions about Referral Program

What does Referral Program do?

Referral and affiliate program design covering referral loop architecture, incentive design, trigger moment optimization, viral coefficient modeling, affiliate program structure, and optimization…. Referral Program is an agent skill from borghei/Claude-Skills. Referral and affiliate program design covering referral loop architecture, incentive design, trigger moment optimization, viral coefficient modeling, affiliate program structure, and optimization playbook.

When should I use Referral Program?

Referral Program fits situations like: moment optimization; viral coefficient modeling; affiliate program structure; optimization playbook.

How do I install Referral Program in Claude Code?

Run `npx skills add borghei/Claude-Skills --skill referral-program -a claude-code`. Or copy the skill folder (business-growth/referral-program in borghei/Claude-Skills) into .claude/skills/referral-program in your project. Claude Code loads it when a task matches its description.

How do I install Referral Program in Codex?

Run `npx skills add borghei/Claude-Skills --skill referral-program -a codex`. Or copy the skill folder (business-growth/referral-program in borghei/Claude-Skills) into .agents/skills/referral-program in your project. Codex loads it when a task matches its description.

Can I use Referral Program in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill referral-program -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/referral-program, .gemini/skills/referral-program, .github/skills/referral-program and .opencode/skills/referral-program in your project.

What does Referral Program need to run?

Going by SKILL.md and its folder, Referral Program needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Referral Program access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Referral Program safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Referral Program use?

Referral Program is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Referral Program use?

About 1.8k tokens (SKILL.md is roughly 7.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.8k tokens, read only when the agent opens those files.

What are the alternatives to Referral Program?

Skills that share tags, products or a category with Referral Program: Referral Program (freekmurze/dotfiles, 1k stars), Churn Prevention (rongxinzy/RongxinAI, 154 stars), 100m Leads (getagentseal/founder-playbook, 721 stars) and Churn Prevention (freekmurze/dotfiles, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Referral Program?

borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 874 GitHub stars. The repository holds 364 skills in this directory. The repository was last updated on October 7, 2026.

Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.