offer-design

name: offer-designdescription: >  Build an offer from the buyer's obstacles: component stack, value drivers,  an affordable guarantee and honest urgency, scored before launch. Use when  buyers say no, packaging bonuses, choosing a guarantee, or naming an offer.license: MIT + Commons Clausemetadata:   version: 1.0.0  author: borghei  category: business-growth  domain: offer-design  updated: 2026-11-08  tags: [offer, value-proposition, guarantee, bonuses, conversion, packaging]

Offer Design

An offer is everything a buyer gets for the price, framed around the result they want. When good products do not sell, the usual cause is that buyers cannot see how the offer gets them past the obstacles they already expect. This skill builds the offer from those obstacles, scores its perceived value, checks that every component earns its cost, prices the guarantee into the contribution, and refuses fake urgency.

The agent works from the buyer's words: obstacles, objections and the result they want come from interviews, reviews or panel answers, not from the seller's feature list.

When to use this skill

• Buyers say no and the product is not obviously the problem
• Packaging a launch offer with bonuses, onboarding help or included extras
• Choosing a guarantee and checking the business can afford it
• Adding urgency without inventing scarcity
• Naming the offer by the outcome it delivers
• Comparing two offer versions before testing them with buyers

For setting the price point, tiers and value metric, use a pricing skill; this one designs what the price buys.

Inputs the skill expects

• Price and core delivery cost per sale
• The result the buyer wants, in their words
• The obstacles the buyer expects on the way to that result
• Candidate components, each with a buyer value and a delivery cost
• Proof available today (results, demonstrations, credentials)
• Refund history or a reasoned claim-rate estimate for any guarantee
• Any real constraint that could justify urgency (cohort dates, capacity)

Clarify First

The obstacle list shapes this output more than anything else: the offer is built to remove them. Settle the inputs below before producing anything. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• The obstacles, in the buyer's words: each one becomes a component; an obstacle invented by the seller produces a component nobody values.
• Delivery cost per component: decides what gets trimmed and whether the guarantee is affordable.
• What proof exists today: belief is the driver most new offers fail on, and the score must not credit proof that does not exist.
• Any real deadline or capacity limit: urgency is only added when a buyer could verify it.

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top, marking estimated values and costs.

Quick Start

python3 business-growth/offer-design/scripts/offer_scorer.py \
  --input business-growth/offer-design/assets/sample_offer.json

python3 business-growth/offer-design/scripts/offer_scorer.py \
  --input business-growth/offer-design/assets/sample_offer_flawed.json --format json

Tools Overview

Stdlib-only and deterministic, with --format text|json. The scoring formula is printed in --help so every number can be checked by hand.

Workflows

Workflow 1 — Build the offer from obstacles

1. Check the market first: pain, budget, reach and momentum (references/offer-construction.md, section 1). If two are weak, rework who the offer is for before designing anything.
2. Write the result the buyer wants in one sentence, in their words.
3. List every obstacle between paying and getting that result: before, during and after. Pull them from interviews, reviews of alternatives or panel objections.
4. For each obstacle, write the component that removes it, its buyer value and its delivery cost.
5. Score the four value drivers from the buyer's point of view: outcome, belief, days to first result, effort.

Workflow 2 — Score, trim and strengthen

1. Run the scorer. Every FAIL must be fixed before the offer is shown to buyers.

python3 business-growth/offer-design/scripts/offer_scorer.py --input offer.json

2. Trim components that return under 3x their cost or answer no obstacle.
3. Add components for unanswered obstacles, cheapest effective fix first.
4. Follow the best lever the scorer names: raise belief with proof, or cut effort and time to the first result.
5. Re-run until the stack is 3x price or more and coverage is complete.

Workflow 3 — Guarantee, urgency and name

1. Pick the strongest guarantee type the contribution can afford, write exact terms, and enter an honest claim rate. The scorer subtracts expected claims from contribution and fails a guarantee that makes sales lose money.
2. Add urgency only from a real constraint (cohort date, capacity cap, published deadline) and mark it verifiable. The scorer fails unverifiable urgency.
3. Name the offer by outcome and timeframe, under twelve words.
4. Fill assets/offer-sheet-template.md.

Workflow 4 — Test with buyers

1. Show the offer to buyers: a simulated panel to surface objections quickly, then a real priced test.
2. Find the objection that still blocks the most buyers, change the one component that answers it, re-score and re-test.
3. Change one thing per round so the result can be attributed.

Validation checkpoint before launch

Use this checklist; each item must pass:

• Scorer exits 0 (no FAIL findings)
• Every obstacle came from buyers, and each is answered
• Belief score is backed by at least two proof items
• Guarantee terms written, claim rate sourced
• Urgency, if any, is verifiable by a buyer

Decision frameworks

Which guarantee

Which lever first

Thresholds the scorer enforces

Anti-Patterns

The Feature Stack

Mistake: Listing everything the product does as bonuses, regardless of what the buyer worries about. Why it happens: The seller knows the features and assumes more equals better. Instead: Start from obstacles. A component that answers no obstacle reads as padding and lowers perceived value; the scorer flags it.

The Unaffordable Guarantee

Mistake: Offering a no-questions refund on a product with high delivery cost and weak onboarding. Why it happens: Strong guarantees lift conversion in case studies, and the claim rate is never estimated. Instead: Price the guarantee in: expected claims times price comes out of contribution. Pick a conditional guarantee when results depend on participation.

The Resetting Countdown

Mistake: Adding a deadline or "only a few left" that is not true. Why it happens: Urgency lifts short-term conversion, and the cost lands later. Instead: Use only constraints a buyer could check: cohort dates, capacity caps, published price changes. Fake scarcity breaks trust and can breach consumer-protection rules.

Competing on Price Alone

Mistake: Responding to "too expensive" by cutting the price. Why it happens: It is the fastest change and the objection names price. Instead: First raise belief, cut effort and add obstacle-removing components; re-test at the same or a higher price. "Too expensive" often means "not sure it will work for me".

Belief Scored on Hope

Mistake: Rating belief 8-9 because the founder is confident. Why it happens: The seller scores from their own view rather than the buyer's. Instead: Score belief from the buyer's side and back anything above 7 with proof. The scorer warns when the score is not earned.

Reference Documentation

• references/offer-construction.md — market check, the four value drivers, building from obstacles, guarantee types and affordability, honest urgency, naming, testing, and offer versus price

Files

Scope & Limitations

In Scope:

• Building an offer from buyer obstacles and scoring it with offer_scorer.py
• Value-driver scoring, component trimming, stack ratio and obstacle coverage
• Guarantee choice with expected claim cost priced into contribution
• Honest urgency and outcome-led naming

Out of Scope:

• Setting the price point, tiers or value metric
• Writing ad copy, landing pages or sales emails
• Running the buyer tests themselves
• Legal review of guarantee or refund terms

Important Caveats:

• Buyer values and driver scores are judgements. The tool makes them explicit and consistent; it does not make them true. Validate with real buyers.
• The value score is a comparison aid between versions of one offer, not a benchmark across businesses.
• Refund and urgency rules vary by market. Have guarantee terms and any deadline or scarcity claims checked against local consumer-protection law.