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 gingiris-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/gingiris-launch)<a href="https://agentmods.dev/skills/gingiris-1031/gingiris-skills/gingiris-launch"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/gingiris-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/gingiris-launch"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/gingiris-launch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 314 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00597 | $0.06014 |
| Opus 5.5 | $0.00239 | $0.02406 |
| Sonnet 5.5 | $0.00119 | $0.01203 |
| Haiku 4.5 | $0.00060 | $0.00601 |
Grade A, and why
gingiris-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 6d 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 — 329 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Launch — GTM Strategy & Product Hunt #1 Playbook
Case-number and citation boundaries:
references/podcast-evidence.md.
产品类型分岔：先选你的路径
你的产品主要用户是谁？
路径 A：开发者 / maker（默认）
→ Product Hunt + GitHub + HN + dev 社区 → 继续阅读下面的 Launch SOP
路径 B：普通消费者（学生/上班族/游戏玩家等）
→ App Store + 垂类社区种子 + 内容获客 → 跳过 Product Hunt（受众错配，ROI 极低） → 冷启动渠道：Reddit 垂类 / 小红书 / TikTok / Quora / 知乎 → 免费产品当钉子（PLG）：先拿到前 100 个真实用户再放量 → 前 100 用户 SOP：在目标社区找近期抒怨同类问题的人 → 私信邀请免费试用 → 用户访谈固化 PMF
路径 C：B2B 决策者
→ LinkedIn 精准建联 + 行业媒体 + HARO → 继续阅读下面的 Launch SOP，重点看 B2B 部分
A launch is a sequence, not an event. The teams that win Product Hunt rarely have the best product that day — they have the best timing, comment cadence, and pre-seeded audience. This playbook is the 14-day operating timeline distilled from 30+ #1 daily launches.
English Version
The 14-day timeline
| Window | What you do |
|---|---|
| T-14 — Prep | Landing page, pricing, FAQ all live. ≥100 early users + email list. Pick one hunter. 3 gallery images + one 30-second demo video ready. |
| T-7 to T-2 — Warm-up | Post a "we're launching, AMA" in the PH discussion board. Tease on X / LinkedIn / Reddit without linking the launch page (don't leak the traffic). Greet your list. Confirm hunter timezone + 00:01 PST post time. |
| T-1 — Final check | Lock every asset. Draft the first comment (<500 words). Prepare 10 FAQ reply templates. Ping 10–20 core supporters for tomorrow morning. |
| T-0 — Launch day | 00:01 PST hunter posts → 00:15 you post the maker first comment → reply in batches every 30 min (never solicit upvotes) → 14:00 PST push the second wave (mailing list + community) → 23:00 PST post a thank-you. |
| T+1 to T+7 — Momentum | Reply to every comment, including the critical ones. Turn the "Featured" screenshot + numbers into a case study. If you hit daily #1, write a "how we won" post and set it as canonical. Import every upvoter email into your user pool. |
What ships with it
3 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.
- 6d ago Changed · +14 lines fcc498c9dfe7
- 1mo ago First seen · 315 lines · 597 tokens per session scan A ddce0b74bb3e
gingiris-launch is a skill published in the GitHub repository Gingiris-1031/gingiris-skills (86 stars, last pushed 6d ago), licensed MIT. It adds 597 tokens to every session and 6,014 once invoked, about $0.0024 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 2026-08-30.
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