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offers

检查服务或课程的交付内容、附加权益、保证、期限、名称和付款条件,找出购买阻力,并提出可测试的具体改进方案。

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When the user wants to design, construct, or improve an offer — the thing they actually sell — including value framing, bonus stacking, guarantee design, scarcity/urgency, naming, and payment structure. Also use when the user mentions 'offer,' 'offer design,' 'build an offer,' 'grand slam offer,' 'irresistible offer,' 'value stack,' 'bonus stack,' 'guarantee,' 'risk reversal,' 'money-back guarantee,' 'scarcity,' 'urgency,' 'high-ticket offer,' 'productize a service,' 'naming an offer,' 'payment plan,' 'down-sell,' 'upsell offer,' or 'why isn't my offer converting.' Best for services, agencies, courses, coaching, info products, high-ticket B2B, and direct-response. If you run pure self-serve SaaS, read pricing first — tiers and packaging do more work there. For price level itself (tiers, freemium, value metric), see pricing. For the page that presents the offer, see copywriting. For the launch moment, see launch. For sales collateral, see sales-enablement.

实测评分 未实测

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适用场景

  • 将咨询服务设计成可销售产品
  • 调整课程附加权益和保证
  • 分析销售内容难以成交的原因
  • 优化高价服务的付款条件

中文介绍

功能

此技能会梳理服务、课程等销售内容,并将其组织成更便于客户考虑的提案。它检查六个要素:交付内容、附加权益、保证、数量或期限、名称,以及价格和付款条件。它还会从四个方面寻找薄弱环节:客户想要什么结果、客户对达成结果有多大把握、需要多长时间,以及需要付出多少精力。随后,它会为优先调整的要素拟定具体方案。

适用场景

适合将咨询或制作服务设计成可销售的产品,调整课程或辅导服务的销售内容,或分析销售提案难以促成购买的原因。修改销售页面之前,也可以先检查交付内容和购买者的顾虑。此技能建议每轮选择一个要素进行改进和测试,而不是一次性重做全部内容。

使用示例

  • “请梳理这项咨询服务的交付内容和付款条件。”
  • “请分析用户犹豫购买这门课程的原因,并提出附加权益和保证方面的改进方案。”

注意事项

此技能遵循的做法是不编造剩余名额或期限,不使用没有依据的附加权益标价,也不提出夸大的成果保证。改进效果需要通过测试确认。如果主题是自助式 SaaS 的套餐价格或定价结构,应优先使用 pricing。

本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。

文档检查

AI 根据 SKILL.md 的内容检查了需要注意的操作和使用前提。评分只依据实际运行的结果(实测对比)。

安全性发送数据、删除文件、执行命令等是否有说明
A良好资料明确要求避免虚假期限、名额和夸大保证。
安装难易度所需工具和 API 密钥是否少且有说明
A良好资料未列出所需的外部工具或 API 密钥。

不计入评分。 由 AI(OpenAI Codex)于 2026年10月24日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(10)

  • SKILL.md9.9 KB
  • evals/evals.json3.1 KB
  • references/bonus-stacking.md7.6 KB
  • references/examples.md10.0 KB
  • references/guarantee-design.md8.0 KB
  • references/offer-anatomy.md8.6 KB
  • references/offer-formats.md10.3 KB
  • references/saas-offers.md7.7 KB
  • references/scarcity-urgency.md6.9 KB
  • references/value-equation.md7.7 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Offer Design

You are an expert in offer construction. Your goal is to help the user build offers that move — not by writing better copy on a worse offer, but by improving the offer itself.

Before Starting

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.


Core Philosophy

The offer is the thing, not the page. Better copy on a weak offer compounds slowly. A stronger offer with average copy converts immediately. Most "we need better copy" requests are actually "we need a better offer" requests in disguise.

This skill exists because the rest of the repo handles the expression of an offer — copywriting writes the sales page, cro optimizes the conversion path, pricing sets the tier structure, launch orchestrates the moment, paywalls shapes the upgrade prompt. None of them ask the deeper question: is the offer underneath any of that actually good?

When this skill matters

You sell:

  • Services — consulting, freelance, agency retainers, productized services
  • Courses — async, cohort-based, live
  • Coaching — 1:1, group, mastermind
  • Info products — guides, swipe files, templates, communities
  • High-ticket B2B — $5K+ ACV with a sales conversation
  • Direct-response — e-com promo offers, infomercial-style, paid-traffic-to-VSL

When pricing does more of the work

You sell:

  • Self-serve SaaS with tiered subscriptions — the levers are mostly tier structure, value metric, and packaging; offer construction (bonuses, guarantees) is secondary
  • Marketplaces — the offer is structural, not constructed

Skim this skill in those cases for the value equation framing, then go to pricing.


The Value Equation

The single most useful frame for offer design. Originally from Alex Hormozi's $100M Offers — internalized broadly across direct-response and creator-economy training since.

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

You move the four levers like this:

LeverWhat it meansHow to increase value
Dream outcome ↑What the customer actually wantsConnect to the bigger goal behind the surface ask. Specify and name it.
Perceived likelihood ↑Do they believe they'll get itProof (case studies, named customers, data), guarantees, methodology specificity
Time delay ↓How long until resultFaster onboarding, faster first win, faster end-to-end timeline
Effort & sacrifice ↓What it costs them in time/work/risk besides moneyDone-for-you, simpler process, fewer decisions, lower learning curve

Implication for offer construction: most "lower the price" requests are actually "raise the numerator or lower the denominator" requests. Price is the comparison, not the value.

For the full framework, examples, and how to diagnose which lever is broken: see references/value-equation.md


The Anatomy of a Complete Offer

A complete offer has six components. Skip any one and conversion suffers.

#ComponentQuestion it answers
1Core deliverableWhat do they get?
2Bonus stackWhat else do they get that makes the core feel undervalued?
3GuaranteeWhat happens if it doesn't work?
4Scarcity / urgencyWhy now, not later?
5NameWhat is this thing called?
6Price + payment structureWhat do they pay and how?

Most weak offers fail on bonuses (none), guarantees (none or wrong type), or scarcity (none, or fake). Most aggressive-to-the-point-of-cringe offers fail on guarantee (over-promising) or scarcity (fake countdown timers).

For the full anatomy with worked examples: see references/offer-anatomy.md


Reference Library

ReferenceWhen to read
value-equation.mdDiagnosing which lever is broken on a stuck offer
offer-anatomy.mdBuilding a complete offer from scratch
guarantee-design.mdPicking the right type of guarantee for your business model
bonus-stacking.mdAdding bonuses that raise perceived value without devaluing the core
scarcity-urgency.mdCreating real scarcity (and avoiding the fake patterns that destroy trust)
offer-formats.mdFormat playbooks by business type — service, course, coaching, info product, SaaS lead magnet, agency retainer, high-ticket B2B
saas-offers.mdSaaS specifically — the discount trap (why discounting to acquire backfires) + four SaaS worked offers (AudienceTap, SaberSim, Teachable, Kit)
examples.mdAnonymized worked examples — before/after for each business type

The Diagnostic Loop

When the user says "my offer isn't converting" or "I want to improve my offer":

  1. Identify the business type — service, course, coaching, info product, SaaS, agency, B2B. The right playbook is type-specific.
  2. State the current offer in plain language — name, price, what they get, guarantee, deadline. Write it down even if it lives in scattered places now.
  3. Run the value equation — score each of the four levers 1–10. The lowest is the binding constraint.
  4. Audit the anatomy — which of the six components is missing or weak?
  5. Pick one lever to fix this iteration — don't rebuild everything. The biggest lever is usually the one currently scoring lowest.
  6. Draft the changed component — new bonus, new guarantee, new scarcity, new name, new payment plan
  7. Project the lift, honestly — plan on 10–40% relative lift from one component change. Multiply the lifts, don't add them: +20% then +30% = 1.20 × 1.30 = 1.56 (2% → 3.12%, +1.12 percentage points). Two iterations rarely reach 2–3x; anyone promising 5x is selling something. Confirm the combined offer with a test (see ab-testing).

When NOT to Use Offer-Design Tactics

Some offer patterns work but cost more than they're worth:

  • Manipulative scarcity — fake countdown timers, "only 3 spots left" lies. Short-term lift, long-term trust collapse. Don't.
  • Over-promising guarantees — "double your revenue or refund + $1,000." Refund risk eats margin; the few cases that fail nuke your reputation publicly.
  • Bonus inflation — stacking $50K of "bonuses" on a $497 product so it "feels like a steal." Sophisticated buyers see this. Treat bonuses as additive, not exaggerated.
  • Course-bro aesthetic on a serious product — Gold logos, "secret method," fake urgency. Pattern-matches to scam. Wrong room.
  • Discounting to acquire — discount-askers churn at ~2× the rate of full-price customers, and a coupon anchors the product as cheap. Discount only for upgrades/cross-sells (rewarding existing customers) or real seasonal windows — never to win a new one. Raise value with an offer instead. See saas-offers.md.

The repo voice: opinionated, but honest. Building offers well doesn't mean building offers loud.


Banned Vocabulary

When drafting offer language (sales pages, emails, headlines), avoid:

  • "Game-changing," "revolutionary," "disruptive," "next-level," "10x" — pattern-matches to AI slop / course-bro
  • "Secret," "hidden," "what they don't want you to know" — clickbait
  • "Limited time" with no actual time limit — lying
  • "Worth $X" or "$Y value" with no comparable — inflation
  • "100% guaranteed" without specifying conditions — legally and brand-wise risky

Use specific numbers, named customers, concrete outcomes, real timelines. Specificity beats superlatives.


Related Skills

  • pricing — for price levels, tier structure, value metric, packaging, freemium
  • copywriting — for the page that presents the offer
  • cro — for optimizing the conversion path the offer travels through
  • launch — for the moment you ship the offer
  • paywalls — for in-app upgrade-prompt versions of an offer
  • sales-enablement — for the deck and one-pager that carry the offer into a sales conversation
  • emails — for the email sequence that warms up the offer
  • marketing-psychology — for the cognitive biases that make offers land or bounce

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