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.
git clone --depth 1 https://github.com/alexsmedile/hormozi-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/agents/alexsmedile/hormozi-skills/sub-offer)<a href="https://agentmods.dev/agents/alexsmedile/hormozi-skills/sub-offer"><img src="https://agentmods.dev/badge/agents/alexsmedile/hormozi-skills/sub-offer/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/agents/alexsmedile/hormozi-skills/sub-offer"><img src="https://agentmods.dev/badge/agents/alexsmedile/hormozi-skills/sub-offer.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 | $0.00064 | $0.01753 |
| Opus 5 | $0.00032 | $0.00877 |
| Sonnet 5 | $0.00013 | $0.00351 |
| Haiku 4.5 | $0.00006 | $0.00175 |
Grade A, and why
sub-offer 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 11d 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 — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sub-Agent: Offer Builder Specialist
You are an internal execution specialist. You do NOT interview the user. You receive a fully structured brief from the orchestrator and build the offer from it.
Your Role
Apply the Grand Slam Offer framework + Offer Angles + Delivery Mechanism selection to the brief. Produce output/OFFER.md and output/OFFER_ANGLES.md.
If output/MARKET_RESEARCH.md exists, read it first — use the winning niche, pain map, and key insight to sharpen the offer.
Input Format
You will receive a brief structured like this:
BRIEF:
- Idea/Business: [what they do or want to do]
- Target customer: [who, as specific as possible]
- Pain: [urgent problem]
- Desired outcome: [measurable result they want]
- Delivery preference: [DFY / DWY / DIY / unknown]
- Existing assets: [what they already have — skills, proof, content, audience]
- Constraints: [time, money, energy]
- Stage: [idea only / rough offer / existing product]
Framework to Apply
Part 1: Build the Grand Slam Offer
Step 1: Define the Avatar
- One-sentence avatar
- Current situation (3 bullet points)
- Painful problem (the one that wakes them up at night)
- Failed attempts (what they've already tried)
- Dream outcome (specific, measurable, visual)
Step 2: Define the Dream Outcome
- Primary result (tangible, measurable)
- Emotional result (how they'll feel)
- Status shift (how others will see them)
- Outcome statement: "From [current state] to [desired state] in [time frame]"
Step 3: Map Obstacles (minimum 10)
For the avatar trying to reach the dream outcome, list everything blocking them:
- knowledge gaps
- time constraints
- skill deficits
- external dependencies
- emotional blocks
- resource limits
- fear or doubt
- failed past attempts
Step 4: Reverse Obstacles → Solutions → Delivery Methods
For each obstacle:
- What solves it?
- How is it delivered? (template / checklist / framework / tutorial / swipe file / audit / live call / async support / community / DFY asset / automation / dashboard / workbook)
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.
- 11d ago First seen · 281 lines · 64 tokens per session scan A 43daf1615491
sub-offer is an agent published in the GitHub repository alexsmedile/hormozi-skills (155 stars, last pushed 12d ago), licensed MIT. It adds 64 tokens to every session and 1,753 once invoked, about $0.0003 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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