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 agentmods add skills/maxtechera/ship/offernpx skills add maxtechera/ship --skill offergit clone --depth 1 https://github.com/maxtechera/shipWhat 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 | $0.00028 | $0.00498 |
| Opus 5 | $0.00014 | $0.00249 |
| Sonnet 5 | $0.00006 | $0.00100 |
| Haiku 4.5 | $0.00003 | $0.00050 |
Grade A, and why
content-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 yesterday.
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.
What it actually says
Content Offer
Engineer offers that convert attention into qualified intent.
Hormozi Value Formula
Value = (Dream Outcome × Perceived Likelihood) ÷ (Time Delay × Effort)
Every offer must explicitly address all four levers.
Scope
- Offer stack and value narrative
- Bonus and guarantee framing
- Objection handling matrix
- Urgency/scarcity logic (real constraints only)
- Lead magnet promise and framing
Inputs Required
- ICP top-ranked pain (from validate.icp)
- Desired conversion event (signup / trial / purchase)
- Pricing constraints and business model
- Available proof assets/testimonials
Required Outputs
- Offer stack copy: headline + 3-5 bullets + guarantee statement
- Objection-response table: top 5 objections with direct responses (per channel surface)
- Lead magnet promise: one-sentence value statement + delivery format
- CTA script variants (3): cold / warm / hot
- Risk reversal language: money-back period, trial length, or guarantee wording
Rules
- Offer must map to one explicit conversion event — no multi-goal offers
- No fake urgency; only real constraints (limited seats, expiring trial, etc.)
- Every objection has a concrete, sourced response (not invented reassurance)
- Keep the promise testable — the customer can verify the claim
Offer Stack Template
[Dream outcome headline]
What you get:
- [Feature → Benefit 1]
- [Feature → Benefit 2]
- [Feature → Benefit 3]
Bonus: [Bonus that removes a specific friction]
Guarantee: [Time-limited, specific risk reversal]
[CTA with urgency if real]
Done Criteria
- Hormozi formula addressed (all 4 levers)
- Offer maps to single conversion event
- Guarantee is specific (not vague "satisfaction guaranteed")
- Objections sourced from ICP research
- No fake urgency or invented scarcity
- 3 CTA variants at different temperatures
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.
- yesterday First seen · 76 lines · 28 tokens per session scan A b421e354ef5a
content-offer is a skill published in the GitHub repository maxtechera/ship (2 stars, last pushed 4mo ago), licensed MIT. It adds 28 tokens to every session and 498 once invoked, about $0.0001 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 2026-08-31.
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