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-solutionsgit 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-solutions)<a href="https://agentmods.dev/skills/calionauta/stelow/stelow-product-solutions"><img src="https://agentmods.dev/badge/skills/calionauta/stelow/stelow-product-solutions/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-solutions"><img src="https://agentmods.dev/badge/skills/calionauta/stelow/stelow-product-solutions.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 | $0.00101 | $0.02745 |
| Opus 5.5 | $0.00040 | $0.01098 |
| Sonnet 5.5 | $0.00020 | $0.00549 |
| Haiku 4.5 | $0.00010 | $0.00275 |
Grade A, and why
stelow-product-solutions 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 — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Solutions: Define and Prioritize
Objective: explore and prioritize possible paths, balancing what is desirable for the audience with what is viable and strategic for the business.
This stage comes after the market is written (stage 2). Candidate solutions are judged against the GPN story, not against your enthusiasm.
Tips:
- Use AI to develop solutions and strategies.
- If exploring convenience products (commodities), evaluate how to innovate in the
product lifecycle — see
stelow-product-evolutionary-principles.
What to do, using the GPN story as reference
- Draft an innovation strategy: differentiated, dominant, sustaining, discreet,
or disruptive (see
referencesofstelow-product-discoveryfor the five types). - Draft potential platforms — the ecosystem or infrastructure behind the product.
- Draft business models — see
stelow-product-business-models. - Draft the main features that address the GPN story.
- Draft the 9 components for a desirable solution (below).
- Evaluate in three aspects: positive impact on the audience, positive impact on the business, level of risk for the business.
"Draft" means 2 to 3 general business-product ideas. It is not a moment to document details or bureaucratize.
The 9 components for a desirable solution
1. Optimize the functional outcome Deliver the best possible result in terms of functionality and performance, within the specific context of use. Understand what success criteria people use to evaluate the functional job to be done.
2. Reduce effort and sacrifice (daily use and lifecycle) Even a functionally superior solution gets rejected if it is too hard to use or demands too much time. This applies to any lifecycle activity: acquire, prepare for first use, maintain, dispose. Start by optimizing acquisition and first use. You can decrease the effort or the perception of it.
3. Reduce time to achievement Solutions that deliver results quickly, or minimize the perception of time through predictability and visible progress, are more attractive — especially under urgency. TTFV — Time to First Value lives inside this component.
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 · 301 lines · 101 tokens per session scan A 3d8e719ec4b5
stelow-product-solutions is a skill published in the GitHub repository calionauta/stelow (14 stars, last pushed 9d ago), licensed MIT. It adds 101 tokens to every session and 2,745 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-solutions compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| stelow-product-solutionscalionauta/stelow | Skill | 14 | 9d ago | MIT | A | 101 | 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 |