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.
npx skills add Infinite-Labs-AI/infinite-skills --skill offer-designgit clone --depth 1 https://github.com/Infinite-Labs-AI/infinite-skillsWrote 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.
[](https://agentmods.dev/skills/infinite-labs-ai/infinite-skills/offer-design)<a href="https://agentmods.dev/skills/infinite-labs-ai/infinite-skills/offer-design"><img src="https://agentmods.dev/badge/skills/infinite-labs-ai/infinite-skills/offer-design/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.
<a href="https://agentmods.dev/skills/infinite-labs-ai/infinite-skills/offer-design"><img src="https://agentmods.dev/badge/skills/infinite-labs-ai/infinite-skills/offer-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.00541 |
| Opus 5 | $0.00018 | $0.00270 |
| Sonnet 5 | $0.00007 | $0.00108 |
| Haiku 4.5 | $0.00004 | $0.00054 |
Grade A, and why
offer-design 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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Offer Design
Shape the offer around buyer anxiety, adoption path, fulfillment constraints, and plan boundaries.
Understand The Sale
Identify:
- Buyer and user.
- Desired outcome.
- Current alternative.
- Price or expected budget.
- Purchase anxiety.
- Fulfillment cost and margin risk.
- Proof available.
- Sales motion: self-serve, sales-led, product-led, service-led, marketplace.
If the user asks for copy before the offer is clear, define the offer first.
Inspect Offer Fit
Break it into fit dimensions:
- Buyer anxiety: what makes the purchase feel risky.
- Adoption path: what has to happen after purchase for value to appear.
- Fulfillment constraint: what the business must deliver reliably.
- Support burden: where customers will need help.
- Abuse risk: how guarantees, trials, discounts, or unlimited use can be misused.
- Plan boundary: what separates small, growing, and high-value customers.
- Proof threshold: what evidence the buyer needs before paying.
- Price logic: how price maps to value, usage, or urgency.
Flag weak spots: vague deliverables, fake urgency, unpriced complexity, guarantees that create abuse, discounts that train waiting, support promises the team cannot honor, or plan tiers that punish the best customers.
Redesign
Offer 2-3 structures:
- Simple offer: easiest to understand and buy.
- Proof-heavy offer: best when trust is the barrier.
- Expansion offer: best when land-and-expand matters.
For each, define the promise, included pieces, exclusions, price logic, risk reversal, and reason to act now.
Watch Outs
- Do not invent scarcity.
- Do not recommend a guarantee the business cannot honor.
- Tie bonuses to purchase anxiety, not random perceived value.
- Use a value metric customers can understand and the business can support.
- Keep plan differences based on customer maturity or usage, not arbitrary feature hiding.
Output
Offer fit diagnosis:
[what is clear, what is weak]
Purchase anxiety:
- [fear]
- [fear]
Recommended offer:
Name:
For:
Promise:
Includes:
Excludes:
Price logic:
Adoption support:
Risk handling:
Abuse boundary:
Plan boundary:
Alternative structures:
1. [option] - [when to use]
2. [option] - [when to use]
Copy-ready bullets:
- [bullet]
- [bullet]
- [bullet]
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 89 lines · 36 tokens per session scan A b9e4b7ab02be
offer-design is a skill published in the GitHub repository Infinite-Labs-AI/infinite-skills (44 stars, last pushed 13d ago), licensed MIT. It adds 36 tokens to every session and 541 once invoked, about $0.0002 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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