Grand Slam Offer

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 2515ce9634da · 15 Oct 2026Report an issue or request a re-scan

• PublisherAffitor GitHub organizationOwner of the repository this skill is read from
• Source folderAffitor/affiliate-skills/skills/landing/grand-slam-offerThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict2515ce9634daA new audit runs when the source changes
• Repository statusNot archivedLast push 2 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Design irresistible affiliate offers using the Hormozi Grand Slam framework. Triggers on: "create an offer for", "design my offer", "grand slam offer", "make an irresistible offer", "why should someone buy through my…

---
name: grand-slam-offer
description: >
  Design irresistible affiliate offers using the Hormozi Grand Slam framework.
  Triggers on: "create an offer for", "design my offer", "grand slam offer", "make an irresistible offer",
  "why should someone buy through my link", "offer framework", "value proposition for",
  "Hormozi offer", "offer stack", "make my offer irresistible", "craft an offer",
  "what makes my offer different", "offer design", "increase perceived value".
license: MIT
version: "1.0.0"
tags: ["affiliate-marketing", "landing-pages", "conversion", "offers", "hormozi"]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
  author: affitor
  version: "1.0"
  stage: S4-Landing
---

# 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

```yaml
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

If product data is available from S1 chaining, use it directly. Otherwise:

1. Use `web_search` to research: `"[product name] features pricing review"`
2. Gather: name, pricing tiers, key features, target audience, top 3 competitors
3. If `target_audience` not provided, infer from product positioning and tags

### Step 2: Apply Value Equation

Read `shared/references/offer-frameworks.md` for the Hormozi framework.

For each component of the Value Equation, score the product 1-10 and identify leverage points:

**Dream Outcome (maximize)**
- What is the #1 transformation the audience wants?
- What does life look like AFTER using this product?
- Frame in terms of identity: "Become the person who..."

**Perceived Likelihood (maximize)**
- What proof exists? (case studies, user count, reviews)
- What specific numbers can you cite?
- What demonstration can you offer? (your own results, screenshots)

**Time Delay (minimize)**
- How fast can they see first results?
- What quick wins does the product offer?
- Can you accelerate with your bonuses? (templates, setup guide)

**Effort & Sacrifice (minimize)**
- What's the learning curve?
- What do they have to give up?
- Can you reduce effort with done-for-you assets?

### Step 3: Design the Offer Stack

Build the complete offer:

1. **Core product** — the affiliate product itself with reframed positioning
2. **Your unique angle** — why YOU are the right person to recommend this
3. **Bonus suggestions** — 3-5 bonuses that address the weakest Value Equation components
4. **Guarantee suggestion** — your personal guarantee on top of the product's
5. **Urgency element** — ethical, real urgency (if applicable)

### Step 4: Write Offer Copy

Create ready-to-use copy blocks:
- **Headline**: One sentence that captures the dream outcome
- **Sub-headline**: Addresses the biggest objection
- **Value stack**: Bullet list of everything they get (product + bonuses + guarantee)
- **CTA**: Action-oriented, specific, urgent

### Step 5: Output

Present the complete Grand Slam Offer framework.

### Step 6: Self-Validation

Before presenting output, verify:

- [ ] Value Equation is complete (all 4 components scored and addressed)
- [ ] Offer is differentiated from a generic "buy through my link" promotion
- [ ] Bonuses are specific and deliverable (not vague promises)
- [ ] Guarantee is realistic and scoped to what YOU can deliver
- [ ] Copy is specific to this product (not generic template fill)
- [ ] FTC-compliant — no income claims, no fake urgency

If any check fails, fix before delivering.

## Output Schema

```yaml
output_schema_version: "1.0.0"
grand_slam_offer:
  product_name: string        # Product being promoted
  value_equation:
    dream_outcome: string     # The transformation promise
    dream_outcome_score: number  # 1-10
    likelihood: string        # Proof points
    likelihood_score: number  # 1-10
    time_delay: string        # Speed to results
    time_delay_score: number  # 1-10 (lower = better, inverted in output)
    effort: string            # Ease of use
    effort_score: number      # 1-10 (lower = better, inverted in output)
    total_value_score: number # Calculated composite

  offer_stack:
    unique_angle: string      #

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-15
   ✓SAFE B 89 / 100

   first audit · source 2515ce9634da

Questions about Grand Slam Offer

• github.com/Affitor/affiliate-skills/tree/main/skills/landing/gra — the audited source at commit 2515ce9634da and every finding aboveObserved · 15 Oct 2026

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