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 calionauta/stelow --skill stelow-product-offergit clone --depth 1 https://github.com/calionauta/stelowWrote 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/calionauta/stelow/stelow-product-offer)<a href="https://agentmods.dev/skills/calionauta/stelow/stelow-product-offer"><img src="https://agentmods.dev/badge/skills/calionauta/stelow/stelow-product-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/skills/calionauta/stelow/stelow-product-offer"><img src="https://agentmods.dev/badge/skills/calionauta/stelow/stelow-product-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 | $0.00106 | $0.02434 |
| Opus 5.5 | $0.00042 | $0.00974 |
| Sonnet 5.5 | $0.00021 | $0.00487 |
| Haiku 4.5 | $0.00011 | $0.00243 |
Grade A, and why
stelow-product-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 10d 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Offer: Develop and Evaluate
Objective: leave the field of ideas and create a tangible offer. Test the value risk: do people show genuine interest when presented with the proposed solution?
This is where the solution stops being a concept and becomes something a person can respond to. Interest is the only thing measured here; payment is stage 5.
Tips:
- Stage 4 and stage 5 are often run together — see
stelow-product-launch-validation. - Use AI to generate ad ideas for online placement.
What to do
- Develop launch promotions, build trust, and develop bonuses — the mechanics live in
stelow-product-promotionsandstelow-product-trust-building. - Experiment with different promoters, starting with yourself.
- Experiment with a combination of channels and marketing messages.
- Structure the search for pioneers (early adopters) with a Pioneer Program.
Pioneer Program — co-creation instead of transaction
Formalize the search for early adopters who want to help build the solution:
- Central offer: extended, free access to the solution (e.g. a full annual plan).
- Clear counterparty: explicit commitment from the pioneer to provide feedback (e.g. one feedback call every X months).
💡 The value offered — complete and free access — attracts people who feel the pain intensely. Whether the commitment in this exchange was real is verified in stage 5.
Types of promoters
1. Yourself.
2. Customers — customers who refer your business.
3. Agencies — agencies that run the prospecting.
4. Affiliates — partners who talk to their own audience about your business:
- Anyone qualified to promote your solution, receiving a percentage of the sale.
- Social media influencers (Instagram, TikTok) — if you are new to the market, contact small influencers (3–10k followers). Analyse real statistics, content types and comments. Offer one of: your solution free plus a discount coupon for the audience; a percentage of sales they generate; or a fixed monthly payment to promote X times per week/month.
- Online affiliate platforms: Hotmart, Kiwify, etc.
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.
- 10d ago First seen · 264 lines · 106 tokens per session scan A 9b8f7a196194
stelow-product-offer is a skill published in the GitHub repository calionauta/stelow (14 stars, last pushed 9d ago), licensed MIT. It adds 106 tokens to every session and 2,434 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-27.
stelow-product-offer compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| stelow-product-offercalionauta/stelow | Skill | 14 | 9d ago | MIT | A | 106 | Claude Code, Codex |
| 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 |