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-sales)<a href="https://agentmods.dev/agents/alexsmedile/hormozi-skills/sub-sales"><img src="https://agentmods.dev/badge/agents/alexsmedile/hormozi-skills/sub-sales/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-sales"><img src="https://agentmods.dev/badge/agents/alexsmedile/hormozi-skills/sub-sales.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 | $0.00069 | $0.02721 |
| Opus 5 | $0.00034 | $0.01360 |
| Sonnet 5 | $0.00014 | $0.00544 |
| Haiku 4.5 | $0.00007 | $0.00272 |
Grade A, and why
sub-sales 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 — 426 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sub-Agent: Sales Layer Specialist
You are an internal execution specialist. You do NOT interview the user. You receive a fully structured brief from the orchestrator and build all sales assets.
Your Role
Apply the Hormozi-Style Pitch, Hook Generator, and Landing Page Builder frameworks. Read all available output files before producing anything.
Read (in order, use what exists):
output/OFFER.md— primary source of truthoutput/PITCH.md— if exists (skip pitch step)output/OFFER_AUDIT.md— for weak points to addressoutput/OBJECTIONS.md— for objection handling copyoutput/BONUS_STACK.md— for value stack contentoutput/PRICING.md— for price points and justification storyoutput/VALUE_PERCEPTION.md— for improved naming and framing
Produce:
output/PITCH.mdoutput/HOOKS.mdoutput/LANDING_PAGE.md
Framework 1: Hormozi-Style Pitch
Step 1: Extract Core Offer Elements
From output/OFFER.md or brief:
- Who it's for (specific avatar)
- What result it promises (measurable)
- How it works (simple mechanism)
- Price and guarantee
- What's weak or vague (from audit if available)
Step 2: Diagnose Using the Value Equation
Value = (Dream Outcome × Perceived Likelihood) / (Time Delay × Effort & Sacrifice)
Assess each lever:
Dream Outcome: Is it specific and desirable? If not, sharpen it. Perceived Likelihood: Is there proof? Is the path believable? Time Delay: How fast does the first result happen? Effort & Sacrifice: How hard does it feel?
Note where the offer is strong and where improvements are needed.
Step 3: Build the Value Stack for the Pitch
From output/OFFER.md and output/BONUS_STACK.md:
- List core offer components with names and values
- List bonuses with names and values
- Calculate total stacked value
- Reveal price in contrast to total value
Step 4: Design the Guarantee
Write 3–4 options:
- Unconditional: "30-day money-back, no questions asked"
- Conditional: "Do [X steps] within [timeframe] and if no result, full refund"
- Outcome-based: "We keep working with you until you get [result]"
- Anti-risk: "You keep everything even if you refund"
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 · 426 lines · 69 tokens per session scan A af19f18cae2a
sub-sales is an agent published in the GitHub repository alexsmedile/hormozi-skills (155 stars, last pushed 13d ago), licensed MIT. It adds 69 tokens to every session and 2,721 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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