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 jpeslar1/john-peslar-ai-skills --skill hundred-million-offersgit clone --depth 1 https://github.com/jpeslar1/john-peslar-ai-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/jpeslar1/john-peslar-ai-skills/hundred-million-offers)<a href="https://agentmods.dev/skills/jpeslar1/john-peslar-ai-skills/hundred-million-offers"><img src="https://agentmods.dev/badge/skills/jpeslar1/john-peslar-ai-skills/hundred-million-offers/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/jpeslar1/john-peslar-ai-skills/hundred-million-offers"><img src="https://agentmods.dev/badge/skills/jpeslar1/john-peslar-ai-skills/hundred-million-offers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00132 | $0.04555 |
| Opus 5.5 | $0.00053 | $0.01822 |
| Sonnet 5.5 | $0.00026 | $0.00911 |
| Haiku 4.5 | $0.00013 | $0.00456 |
Grade A, and why
hundred-million-offers 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 2mo 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.
This is a copy
100% identical to hundred-million-offers — 74 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grand Slam Offer Creation Framework
Framework for creating offers so good people feel stupid saying no. What you sell (the offer) matters more than how you sell it or who you sell it to.
Core Principle
The offer is the #1 lever in any business: a Grand Slam Offer sells despite mediocre marketing, while the best marketing in the world cannot save a bad offer. Before optimizing funnels, running more ads, or hiring salespeople, fix the offer. A Grand Slam Offer maximizes Dream Outcome and Perceived Likelihood of Achievement while minimizing Time Delay and Effort & Sacrifice --- becoming a category of one with no comparable alternative.
Scoring
Goal: 10/10. Score any offer by the 7-row Quick Diagnostic at the end of this file --- award ~1.4 points per row answered "yes," rounding to a 0-10 scale. Bands: 9-10 = all/nearly all rows pass (irresistible: 10x perceived value, reversed risk, ethical scarcity, named dollar-valued bonuses, a category-of-one bundle, a MAGIC name); 5-6 = value and market are right but risk, bonuses, or scarcity are missing; <=3 = a commodity priced on cost with no guarantee or reason to act now. Always report the current score and the specific diagnostic rows that must flip to "yes" to reach 10/10.
The Grand Slam Offer Framework
1. The Value Equation
Core concept: Value = (Dream Outcome x Perceived Likelihood of Achievement) / (Time Delay x Effort & Sacrifice). Maximize the numerator and minimize the denominator to create massive perceived value.
Why it works: People buy outcomes, not products --- they weigh the dream result and their confidence in achieving it against how long and hard the path is. When the numerator vastly outweighs the denominator, the offer feels like a no-brainer regardless of price.
Key insights:
- Dream Outcome defines the ceiling of your value
- Perceived Likelihood often matters more than actual results --- social proof, guarantees, and track record raise it
- Time Delay is a silent killer; faster results command premium prices
- Effort & Sacrifice includes everything the customer gives up (time, comfort, status, identity)
- A guarantee raises Perceived Likelihood and lowers perceived risk simultaneously
What ships with it
10 files 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.
- references/bonuses-stacking.md 12 KB
- references/case-studies.md 17 KB
- references/grand-slam-offers.md 12 KB
- references/guarantees.md 14 KB
- references/naming-offers.md 16 KB
- references/offer-creation-checklist.md 15 KB
- references/pricing-strategy.md 12 KB
- references/scarcity-urgency.md 14 KB
- references/starving-crowd.md 13 KB
- references/value-equation.md 11 KB
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.
- 2mo ago First seen · 310 lines · 132 tokens per session scan A 8c1105e8bba0
hundred-million-offers is a skill published in the GitHub repository jpeslar1/john-peslar-ai-skills (12 stars, last pushed 18d ago), licensed MIT. It adds 132 tokens to every session and 4,555 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to hundred-million-offers, differing in 74 lines, and is treated as a copy.
hundred-million-offers compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| hundred-million-offersjpeslar1/john-peslar-ai-skills | Skill | 12 | 18d ago | MIT | A | 132 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |