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-audience-marketgit 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-audience-market)<a href="https://agentmods.dev/skills/calionauta/stelow/stelow-product-audience-market"><img src="https://agentmods.dev/badge/skills/calionauta/stelow/stelow-product-audience-market/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-audience-market"><img src="https://agentmods.dev/badge/skills/calionauta/stelow/stelow-product-audience-market.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 | $0.00129 | $0.02470 |
| Opus 5.5 | $0.00052 | $0.00988 |
| Sonnet 5.5 | $0.00026 | $0.00494 |
| Haiku 4.5 | $0.00013 | $0.00247 |
Grade A, and why
stelow-product-audience-market 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 — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audience and Market Definition
Two stages of the short-cycle method. Stage 1 explores the problem space; stage 2 turns what you learned into a market you can act on. Both come before any solution thinking — if you skip them you will build for an audience you invented.
Core rule: in stage 1 you are not validating your idea. You are understanding people. The moment you mention your solution, the conversation stops being evidence.
Stage 1 — Find and Understand the Audience
Objective: explore the "problem space" — the contexts, difficulties, and needs of people who are actively trying to solve something. It is not to validate your idea, but to understand the audience deeply.
Tips:
- If you already have a solution idea, it is still worth going through this stage.
- If you lack time or resources, explore with AI — especially the purposes people want to accomplish and the needs attached to them.
- Useful tools: GummySearch, AnswerThePublic, Glimpse, Google Trends, Globe.engineer.
SEDIF — where to find people
S=Follow, E=Entertain, D=Invest Money, I=Go/Attend, F=Frequent — four practical dimensions (D and I collapse into "invest"):
- Follow: who do they follow, which people inspire them? (friend network, work colleagues, authors and influencers on social media)
- Entertain: how do they have fun? (hobbies, places)
- Invest: what do they invest money and time in? (conferences, events, stores that sell the resources they use)
- Frequent: what places do they frequent, physical or virtual, to talk about the subject? (forums and communities — Facebook, Reddit, etc.)
Create a space to gather people
- Webinar on the topic — to register, ask for name and contact.
- WhatsApp or Telegram group for people who want to discuss the topic (or a Discord server).
- Quiz on the topic helping the person evaluate their knowledge — to receive the result, ask for name and email. Tools: Tally, Scoreapp, Limesurvey (open-source).
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 · 265 lines · 129 tokens per session scan A 05e4fb90c51b
stelow-product-audience-market is a skill published in the GitHub repository calionauta/stelow (14 stars, last pushed 9d ago), licensed MIT. It adds 129 tokens to every session and 2,470 once invoked, about $0.0005 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-audience-market compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| stelow-product-audience-marketcalionauta/stelow | Skill | 14 | 9d ago | MIT | A | 129 | 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 |