Agent skill

100m Offers

by getagentseal in getagentseal/founder-playbook

Designs irresistible Grand Slam Offers using Alex Hormozi's value equation, obstacle-solution mapping, value-cost trim/stack, and offer-naming patterns.

MITAuto-check passedMarketing & SEO

Install 100m Offers

skills CLI
$ npx skills add getagentseal/founder-playbook --skill 100m-offers -a claude-code

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

GitHub CLI
$ gh skill install getagentseal/founder-playbook 100m-offers --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/getagentseal/founder-playbook.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/100m-offers .claude/skills/100m-offers && 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
100m-offers
GitHub stars
729
Token cost
~2.7k tokens
SKILL.md length
1,315 words
Files
5
Skills in repo
16
Repo updated
First seen
Licence
MIT

At a glance

Designs irresistible Grand Slam Offers using Alex Hormozi's value equation, obstacle-solution mapping, value-cost trim/stack, and offer-naming patterns.

  • Fixing low conversion rates
  • SKILL.md covers The Core Insight, Decision Tree, Step 0: Pick a Starving Crowd and The Value Equation, plus 12 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Packaging products

What it does

100m Offers is an agent skill from getagentseal/founder-playbook. Designs irresistible Grand Slam Offers using Alex Hormozi's value equation, obstacle-solution mapping, value-cost trim/stack, and offer-naming patterns. Use when fixing low conversion rates, packaging products, pricing services, designing high-converting offers, or when prospects browse but don't buy. Covers value stacking, guarantees, scarcity, urgency, and pricing psychology for validated markets.

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `cases.md`, `examples.md` and `frameworks.md`).

It sits in Marketing & SEO, covering Conversion rate optimization. The repository describes itself as: 15 proven business books distilled into AI-native skills. Works with Claude Code, ChatGPT, Gemini, Cursor. The licence is MIT.

When your agent uses it

  • Fixing low conversion rates
  • Packaging products
  • Pricing services
  • Designing high-converting offers

Example prompts

  • “Use the 100m-offers skill to design irresistible Grand Slam Offers using Alex Hormozi's value equation, obstacle-solution mapping, value-cost…”
  • “/100m-offers”

What it can do on your machine

Read from SKILL.md and the folder at commit e811e63. 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

    No scripts in the folder and no shell commands in SKILL.md.

    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

100m Offers loads about 2.7k tokens when it runs. Until then it costs about 104 tokens; SKILL.md has 1,315 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~104
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from getagentseal/founder-playbook at commit e811e63, republished under its MIT licence (© getagentseal). 1,315 words, ~2,680 tokens.

Download SKILL.mdSave it as .claude/skills/100m-offers/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
100m-offers
description
Designs irresistible Grand Slam Offers using Alex Hormozi's value equation, obstacle-solution mapping, value-cost trim/stack, and offer-naming patterns. Use when fixing low conversion rates, packaging products, pricing services, designing high-converting offers, or when prospects browse but don't buy. Covers value stacking, guarantees, scarcity, urgency, and pricing psychology for validated markets.

Note: This skill is independent analysis and commentary, not a reproduction of the original text. It synthesizes the book's core ideas with modern startup practice, surfaces where frameworks are outdated or incomplete, and integrates perspectives from adjacent disciplines. For the full argument and context, read the original book.

$100M Offers

"Make offers so good people feel stupid saying NO" - Alex Hormozi

The Core Insight

"You don't have a sales problem or a marketing problem. You have an offer problem."

If qualified leads aren't buying, it's not your traffic - it's what you're offering them.

Decision Tree

Are you getting qualified leads?
├─ NO → Use 100m-leads
└─ YES → Are leads converting?
         ├─ NO → You have an offer problem (this skill)
         └─ YES → Are you charging premium?
                  ├─ NO → Stack value, raise prices
                  └─ YES → Scale ad spend
Building an offer?
├─ Pre-validation → Use mom-test FIRST
├─ Validated market → Use this skill
├─ Pricing question only → See monetizing-innovation
└─ Positioning unclear → See obviously-awesome

Step 0: Pick a Starving Crowd

Before any offer work: a great offer in a dead market fails. A mediocre offer in a ravenous market can still win.

The core principle: You want a market that is already hungry - people actively searching for a solution and willing to pay for it. Market selection is the multiplier on everything that follows.

3 criteria to test any market:

CriterionQuestion to askRed flag
PainDo they have an urgent, specific problem?Vague dissatisfaction, not a burning need
Purchasing powerCan they actually pay for a solution?Market with desire but no budget
TargetabilityCan you reach them efficiently?Audience that is scattered or hard to identify

All three must be true. Two out of three is not enough.

How to pick:

  1. List markets where you have access or credibility.
  2. Run each through the 3-criteria test above.
  3. Pick the one with the highest pain score and a clear place to find them.

A growing market also covers execution mistakes. A shrinking market punishes even excellent offers.

Rule: Niche selection is a prerequisite, not a step inside offer-building. Don't start the value equation until you know exactly who you are selling to and why they are desperate to buy.

The Value Equation

              Dream Outcome × Perceived Likelihood
   Value  =  ─────────────────────────────────────
                  Time Delay × Effort & Sacrifice

Four levers. Pull any of them; both directions work.

LeverDirectionHow
Dream Outcome↑ BiggerMake the outcome more compelling, specific, emotional
Perceived Likelihood↑ HigherAdd proof, guarantees, testimonials, credentials
Time Delay↓ FasterShow results sooner, deliver quicker
Effort & Sacrifice↓ EasierRemove steps, do it for them, automate

Rule: If you can't increase the top, decrease the bottom.

What Makes a Grand Slam Offer

A Grand Slam Offer is incomparable to competitors because it combines:

  1. Attractive promotion - The hook
  2. Unmatchable value proposition - The stack
  3. Premium price - Price implies value
  4. Unbeatable guarantee - Removes risk

If anyone can compare your offer to a competitor's, you don't have a Grand Slam Offer yet.

The 6-Step Process

StepActionOutput
1Define dream outcomeSpecific result + timeframe + emotional benefit
2List ALL obstacles15+ minimum, across 4 categories
3Reverse obstacles to "How to..." solutionsSolution statement per obstacle
4Design delivery vehiclesFormat, frequency, intensity per solution
5Trim and stack (value-cost matrix)6-10 strong components
6Add enhancementsGuarantees, scarcity, urgency, bonuses

Detail in frameworks.md.

Step 1: Dream Outcome Format

[Specific result] in [timeframe] so that [emotional benefit/status change]

BadGood
Lose weightLose 20 lbs in 8 weeks so you can wear that dress at your reunion
Make more moneyAdd $30K/mo in 90 days so you can quit your day job
Get more leadsBook 10 sales calls per week without cold calling
Step 2: 4 Obstacle Categories
CategoryWhat It BlocksExample
Knowledge GapsWhat they don't KNOW"I don't know what to eat"
Skill DeficienciesWhat they can't DO"I can't cook healthy meals"
Environmental BarriersExternal factors"My family won't eat this food"
Psychological BlocksMindset issues"I always quit after 2 weeks"

Hormozi's wording for environmental is "External" - same concept.

Step 5: Value-Cost Decision Matrix

Score each component on Value to customer (1-10) and Cost to deliver (1-10). High-value, low-cost components stay in the core offer; mid-value components move to bonuses; low-value or high-cost components get cut. Target 6-10 strong components total.

Full value-cost matrix and scoring: see frameworks.md.

The Stacking Principle

"A single offer is less valuable than the same offer broken into its component parts and stacked as bonuses."

Bad Presentation

"$2,000 weight loss program"

Good Presentation
  • Personal nutrition orientation ($500 value)
  • Recorded grocery store tour ($200 value)
  • Weekly meal plans ($600 value)
  • Accountability buddy system ($400 value)
  • Restaurant eating system ($300 value)
  • Travel workout plans ($250 value)
  • 20-pound guarantee (PRICELESS)
  • Total Value: $2,250+
  • Your Investment: $599

Same product. Substantially higher perceived value.

Pricing Psychology

Charge More, Not Less
High PriceLow Price
Signals high valueSignals low value
Forces you to deliver high valueLets you cut corners
Premium customers complain lessCheap customers most demanding
Price Anchoring (Always Show Three Numbers)
  1. Total Value: $X (sum of all components)
  2. Your Investment: $Y (the price)
  3. You Save: $Z = $X - $Y

The bigger the gap between value and investment, the more "no-brainer" the offer feels.

Show full SKILL.md (511 more words)Show less
Value-to-Price Rule

Hormozi's directional guidance is to make total stated value substantially higher than the price - he tends to use 10x or "incomparable" framings rather than a precise multiplier. Aim for stacked value that makes the price feel small in comparison. If your offer's stated value is only ~2x the price, it's not yet a Grand Slam Offer.

Critical Warning: Validate Market First

Hormozi's framework assumes you have product-market fit. The book doesn't tell you this.

If you build a Grand Slam Offer for a market that doesn't want your thing, the offer will fail no matter how good.

Before building the offer:

  1. Have 5-10 customer interview transcripts (use mom-test)
  2. Validate dream outcome with actual customer language
  3. Confirm target market has budget
  4. Survey data with specific responses

The framework is for VALIDATED markets. If you're pre-validation, use mom-test first.

Common Mistakes

MistakeFix
Vague solutions ("Coaching")Specific delivery ("Weekly 30-min strategy calls + custom action plans")
Including everythingTrim aggressively. 6-10 strong > 20 mediocre
Building on assumptionsUse mom-test customer language
UnderpricingIf incomparable, premium is mandatory
Generic namesSpecific transformation names sell

Success Signals

You have a Grand Slam Offer when:

  1. Prospects stop comparing you to competitors (you're incomparable)
  2. Conversion rates improve materially (Hormozi typically reports 2-3x lift; specific numbers vary by business)
  3. You can raise prices and customers still say yes
  4. People feel stupid saying no

Rules of Thumb

  1. Solve every obstacle. Not most. Every. One.
  2. Premium price = premium product. People judge value by price.
  3. Trim ruthlessly. Cut anything below 8/10 value.
  4. Stack, don't simplify. Break the offer into named components.
  5. Validate market first. Don't build for a fantasy.
  6. Names sell. Generic = invisible. Specific = irresistible.
  7. Guarantees remove risk. Bigger guarantee = bigger price you can charge.
  8. Show the math. Total value, price, savings. Always.

When This Doesn't Apply

ContextWhy
Pre-validationGet product-market fit first (use mom-test)
Commodity productsHard to differentiate sugar or coffee beans
Low-ticket impulse buysBelow ~$50, simpler offers work better
Regulated industriesCompliance may limit stacking and guarantees
Pure SaaS subscriptionsStacking applies less; focus on outcomes per seat

The Order

  1. Find dream outcome (in their words from mom-test interviews)
  2. List 15+ obstacles (4 categories)
  3. Reverse each into "How to..." solution
  4. Design delivery for each
  5. Score value vs cost
  6. Trim and stack
  7. Name with offer-naming formula
  8. Add guarantees / scarcity / urgency
  9. Present with value-price-savings math

The Test

When prospects see your offer, they should feel:

  • "This solves every problem I have"
  • "There's no way this is only $X"
  • "I'd be stupid to say no"

If they don't, the offer needs more work.

Supporting Files

  • frameworks.md - Detailed step-by-step processes, delivery vehicle dimensions, naming formula (MAGIC), enhancements (guarantees, scarcity, urgency, bonuses)
  • cases.md - Hormozi's gym case study with verification notes, before/after examples
  • examples.md - Bundle naming patterns, one-page offer template, real offer examples
  • integration.md - How offers connect to mom-test (input) and 100m-leads (output); conflicts with monetizing-innovation pricing approaches and how the offer feeds money-models (offer sequencing)

© getagentseal, 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 4 other files in skills/100m-offers of getagentseal/founder-playbook.

  • SKILL.md
  • cases.md
  • examples.md
  • frameworks.md
  • integration.md

Open the folder on GitHubat commit e811e63

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Categories

Questions about 100m Offers

What does 100m Offers do?

Designs irresistible Grand Slam Offers using Alex Hormozi's value equation, obstacle-solution mapping, value-cost trim/stack, and offer-naming patterns. 100m Offers is an agent skill from getagentseal/founder-playbook. Designs irresistible Grand Slam Offers using Alex Hormozi's value equation, obstacle-solution mapping, value-cost trim/stack, and offer-naming patterns.

When should I use 100m Offers?

100m Offers fits situations like: fixing low conversion rates; packaging products; pricing services; designing high-converting offers.

How do I install 100m Offers in Claude Code?

Run `npx skills add getagentseal/founder-playbook --skill 100m-offers -a claude-code`. Or copy the skill folder (skills/100m-offers in getagentseal/founder-playbook) into .claude/skills/100m-offers in your project. Claude Code loads it when a task matches its description.

How do I install 100m Offers in Codex?

Run `npx skills add getagentseal/founder-playbook --skill 100m-offers -a codex`. Or copy the skill folder (skills/100m-offers in getagentseal/founder-playbook) into .agents/skills/100m-offers in your project. Codex loads it when a task matches its description.

Can I use 100m Offers 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 getagentseal/founder-playbook --skill 100m-offers -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/100m-offers, .gemini/skills/100m-offers, .github/skills/100m-offers and .opencode/skills/100m-offers in your project.

What does 100m Offers need to run?

SKILL.md names no scripts, command-line tools or credentials: 100m Offers is instructions for the agent only.

Does 100m Offers 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 100m Offers 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. Review the folder before installing.

What licence does 100m Offers use?

100m Offers is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does 100m Offers use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to 100m Offers?

Skills that share tags, products or a category with 100m Offers: FLOW SEO Framework (AgriciDaniel/claude-seo, 19k stars), Paywalls (coreyhaines31/marketingskills, 54k stars), Onboarding Cro (freekmurze/dotfiles, 1k stars) and Paywall Upgrade Cro (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 100m Offers?

getagentseal (a GitHub organization) maintains it in getagentseal/founder-playbook, which has 729 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 6, 2026.

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