grand-slam-offer

grand-slam-offer is a skill for Claude Code, Codex from Gingg7260/affiliate-skills. It costs 100 tokens per session (2,585 once invoked), scanned A, a copy of grand-slam-offer, MIT.

A framework for shaping an affiliate promotion so customers understand its value and why they should use one particular affiliate link. It uses the Hormozi Value Equation, which weighs the desired result and expected success against waiting time and effort.

In plain words
What is it for?
Use it to define an affiliate offer, value proposition, offer bundle, or promotional angle for a product and its landing page.
Why use it?
It helps address the problem of promoting the same product as many other affiliates without a clear reason to choose your offer. It creates the positioning needed before writing a landing page.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: mentions Claude Code; built for openclaw; mentions Gemini CLI.

Good fit Use it to define an affiliate offer, value proposition, offer bundle, or promotional angle for a product and its landing page.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gingg7260/affiliate-skills/grand-slam-offer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Gingg7260/affiliate-skills --skill grand-slam-offer
Clone the repo
git clone --depth 1 https://github.com/Gingg7260/affiliate-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for grand-slam-offer

README.md
[![agentmods](https://agentmods.dev/badge/skills/gingg7260/affiliate-skills/grand-slam-offer/github.svg)](https://agentmods.dev/skills/gingg7260/affiliate-skills/grand-slam-offer)
Your own site
<a href="https://agentmods.dev/skills/gingg7260/affiliate-skills/grand-slam-offer"><img src="https://agentmods.dev/badge/skills/gingg7260/affiliate-skills/grand-slam-offer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for grand-slam-offer

Your own site · 80×15
<a href="https://agentmods.dev/skills/gingg7260/affiliate-skills/grand-slam-offer"><img src="https://agentmods.dev/badge/skills/gingg7260/affiliate-skills/grand-slam-offer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,585 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00100 $0.02585
Opus 5 $0.00050 $0.01293
Sonnet 5 $0.00020 $0.00517
Haiku 4.5 $0.00010 $0.00259

Measured 12d ago against content hash 95ff9e4068a9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

grand-slam-offer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to grand-slam-offer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/landing/grand-slam-offer/SKILL.md · 270 lines

How it starts

The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Grand Slam Offer

Design affiliate offers so good people feel stupid saying no. Uses the Hormozi Value Equation: Value = Dream Outcome × Perceived Likelihood ÷ Time Delay ÷ Effort & Sacrifice. Deconstructs why someone should click YOUR link over any other affiliate's.

Stage

S4: Landing — The offer IS the landing page's job. Before writing HTML or copy, you need an offer framework that makes the conversion inevitable.

When to Use

  • User wants to differentiate their affiliate promotion from every other affiliate
  • User asks "why would someone buy through MY link?"
  • User is about to create a landing page and needs the offer angle first
  • User wants to increase conversion rates on an existing promotion
  • User says anything like "offer", "value proposition", "irresistible", "Hormozi"
  • User has a product from S1 and wants to craft the positioning before S4 landing page

Input Schema

product:                    # REQUIRED — the affiliate product
  name: string              # Product name
  description: string       # What it does
  reward_value: string      # Commission (e.g., "30% recurring")
  url: string               # Affiliate link URL
  pricing: string           # Product price or pricing page URL
  tags: string[]            # e.g., ["ai", "video", "saas"]

target_audience: string     # OPTIONAL — who you're targeting
                            # Default: inferred from product tags

bonuses: string[]           # OPTIONAL — bonuses you're already offering
                            # Default: none (will suggest bonuses)

competitors: string[]       # OPTIONAL — competing products
                            # Default: auto-researched

Chaining from S1: If affiliate-program-search was run earlier, automatically pick up recommended_program as the product input.

Chaining from S1 purple-cow-audit: If purple-cow-audit was run, use remarkability_score and remarkable_angles to inform the offer.

Workflow

Step 1: Gather Context

Read the full file on GitHub · 270 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 270 lines · 100 tokens per session scan A 95ff9e4068a9

Subscribe to this mod's changes

grand-slam-offer is a skill published in the GitHub repository Gingg7260/affiliate-skills (5 stars, last pushed 2d ago), licensed MIT. It adds 100 tokens to every session and 2,585 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to grand-slam-offer, differing in 0 lines, and is treated as a copy.

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