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 Gingiris-1031/gingiris-skills --skill ai-product-launchgit clone --depth 1 https://github.com/Gingiris-1031/gingiris-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/skills/gingiris-1031/gingiris-skills/ai-product-launch)<a href="https://agentmods.dev/skills/gingiris-1031/gingiris-skills/ai-product-launch"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/ai-product-launch/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/gingiris-1031/gingiris-skills/ai-product-launch"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/ai-product-launch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00050 | $0.01083 |
| Opus 5 | $0.00025 | $0.00541 |
| Sonnet 5 | $0.00010 | $0.00217 |
| Haiku 4.5 | $0.00005 | $0.00108 |
Grade A, and why
ai-product-launch 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⚠️ 2C 产品慎用 Product Hunt
本 skill 默认 dev / maker / B2B 受众。2C 消费品 / 教育 / 应用 / 游戏：Product Hunt 受众是 maker/科技人，与普通消费者错配、ROI 极低——最多发一次拿个 badge。请把精力换到 垂类社区（Reddit / 小红书 / 知乎 / Naver 카페）+ 短视频（TikTok / 抖音 / YouTube）+ 垂直 KOL，并用"免费产品当钩子"做转化。完整 2C 渠道数据库（各国 MAU + 公开来源）+ 三路 Launch 分叉见 → gingiris-seo-geo/references/2c-adaptation.md（及 gingiris-launch）。
First AI Product Launch — Zero to Live in 30 Days
📦 Install
clawhub install ai-product-launch
What you get after installing:
- Structured 30-day framework from idea to paying users
- Week-by-week deliverables and validation milestones
- Common pitfalls first-time AI founders hit (and how to avoid them)
A structured 30-day framework that takes you from "I have an AI idea" to "I have paying users." Each week has clear deliverables, so you never get stuck wondering what to do next.
What You'll Learn
- Week 1: Validation (problem interviews, competitor audit, positioning)
- Week 2: Build (MVP scoping, AI model selection, landing page)
- Week 3: Pre-launch (beta cohort, feedback loops, content seeding)
- Week 4: Launch & iterate (distribution, metrics, first revenue)
- Common pitfalls first-time AI founders hit (and how to avoid them)
Who This Is For
- First-time founders building AI/ML products
- Developers going from side project to real product
- Anyone who wants a structured 30-day launch roadmap
Week 4 Deep-Dive: Distribution Math (2026 benchmarks)
Most first launches fail on arithmetic, not effort. Three rules before spending a dollar:
1. Work backwards from a traffic number. Fix the launch-day visitor target first, then assign every channel a hard quota with its own UTM short link and an owner. Reference case (an AI Agent product, Dec 2025): a 500K-visit launch day — roughly 80% from creator quote-tweets, the remainder split across Reddit threads, YouTube ads, and community groups — netted ~800 signups at $6.25 per registration. Impressions are cheap to engineer; conversion is the constraint.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 84 lines · 50 tokens per session scan A b986e53f216b
ai-product-launch is a skill published in the GitHub repository Gingiris-1031/gingiris-skills (79 stars, last pushed 5d ago), licensed MIT. It adds 50 tokens to every session and 1,083 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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