grand-slam-offer

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

An offer-planning framework for explaining why someone should choose your affiliate link for a product. An affiliate link tracks referrals so the seller can credit you for a commission.

In plain words
What is it for?
Creating value propositions, offer angles, and bonus or positioning ideas for affiliate promotions.
Why use it?
It helps turn a basic product recommendation into a clearer reason to act, such as a defined result, lower uncertainty, or added value. It is intended to shape the offer 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 Creating value propositions, offer angles, and bonus or positioning ideas for affiliate promotions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/affitor/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 Affitor/affiliate-skills --skill grand-slam-offer
Clone the repo
git clone --depth 1 https://github.com/Affitor/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/affitor/affiliate-skills/grand-slam-offer/github.svg)](https://agentmods.dev/skills/affitor/affiliate-skills/grand-slam-offer)
Your own site
<a href="https://agentmods.dev/skills/affitor/affiliate-skills/grand-slam-offer"><img src="https://agentmods.dev/badge/skills/affitor/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/affitor/affiliate-skills/grand-slam-offer"><img src="https://agentmods.dev/badge/skills/affitor/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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found 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 2026-09-11, 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

Copies of this mod

2 near-identical copies found in the catalogue:

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 Affitor/affiliate-skills (658 stars, last pushed 6d 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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