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 agentmods add skills/gingiris/gingiris-skills/gr-ph-launchnpx skills add Gingiris/gingiris-skills --skill gr-ph-launchgit clone --depth 1 https://github.com/Gingiris/gingiris-skillsWhat 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 | $0.00095 | $0.01135 |
| Opus 5 | $0.00048 | $0.00567 |
| Sonnet 5 | $0.00019 | $0.00227 |
| Haiku 4.5 | $0.00010 | $0.00113 |
Grade A, and why
gr-ph-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 3d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gr-ph-launch — Product Hunt 发布
深度参考
详细方法论与案例库见：
references/framework.md—— 14 天时间线 + 5 大板块references/hunter-playbook.md—— hunter 选择矩阵references/maker-comment.md—— maker comment 6 种模板
注：首次使用时 Claude 应读
references/framework.md再开始。
14 天时间线（精简版）
T-14：准备
- 产品页面就绪（落地页、定价、FAQ）
- 已有 ≥ 100 早鸟用户 + 邮箱列表
- 选 1 个 hunter（优先级：活跃 > follower 数）
- 筹备 3 张主 gallery 图 + 1 个 30 秒 demo video
T-7 到 T-2：预热
- PH discussion 板块发 1 篇"we're launching, AMA"
- Twitter / LinkedIn / Reddit 预告（不贴 launch 页面链接，避免抢流量）
- Newsletter 给用户打招呼
- 确认 hunter 时区 + 发布时间（PST 00:01）
T-1：终检
- 所有 asset 最终版锁定
- 准备 first comment 文稿（500 字以内）
- 准备 10 条可能的 FAQ 回复模板
- 联系 10-20 个核心支持者提醒明早
T-0：发布日
- 00:01 PST hunter 发布
- 00:15 maker（你）发 first comment
- 整天：每 30 分钟回一批评论；不刷 upvote
- 14:00 PST：peak time，推 second wave（通讯录 + community）
- 23:00 PST：总结发一条感谢帖
T+1 到 T+7：momentum
- 回完所有评论（包括差评）
- 把 PH featured 截图 + 数据做成 case study
- 若进入 daily #1 → 做一篇"how we won"博客（
gr-blog-post） - 将所有 upvoter 邮箱导入用户池
Hunter 选择矩阵
| 优先级 | 标准 |
|---|---|
| P0 | 最近 30 天有成功 hunt 且产品领域相似 |
| P1 | Follower 1k+ 且活跃（每周发帖） |
| P2 | 跟你有真实社交关系（聊过、共同朋友） |
| ❌ | Follower > 50k 但已 6 个月没 hunt |
搜 hunter：https://www.producthunt.com/@<category>-hunters
Maker Comment 6 种模板
（详见 references/maker-comment.md）
- Founder story：为什么做 → 你的痛 → 解法
- Before / After：有你的产品前 vs 后，对比鲜明
- Key stats hook：一个爆炸性数据开头
- Honest limitation：承认产品还不完美，显得真实
- Community thanks：感谢参与测试的用户（点名 @）
- Tech stack pride：讲技术选型（适合开发者向产品）
选哪个：看目标受众。To dev → 6；to general → 1 或 3。
反踩雷清单
- ❌ 不要在 launch 当天还在改产品（flaky）
- ❌ 不要让家人朋友刷 upvote（PH shadowban 很快）
- ❌ 不要在 first comment 留外部链接（触发审核降权）
- ❌ 不要忘记 mobile 截图（40% 流量来自手机）
- ❌ 不要硬撑 daily #1，#2-#3 也是成功（且少被喷）
级联推荐
- 发布日 + 24h →
gr-seo-patrol监控" product hunt"等关键词排名 - 上 daily #1 后 →
gr-blog-post写一篇"how we won"并设 canonical 为主 - 评论有大量质疑 →
gr-user-interview启动正式访谈
API 依赖
| Service | Env var |
|---|---|
| Product Hunt（公开页爬取） | 无（用 web-access skill） |
| GitHub PAT（博客联动） | GITHUB_TOKEN |
What ships with it
1 file 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.
- 3d ago First seen · 113 lines · 95 tokens per session scan A 45951f689523
gr-ph-launch is a skill published in the GitHub repository Gingiris/gingiris-skills (23 stars, last pushed 3mo ago), licensed MIT. It adds 95 tokens to every session and 1,135 once invoked, about $0.0005 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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