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-1031/gingiris-skills/gingiris-github-star-growthnpx skills add Gingiris-1031/gingiris-skills --skill gingiris-github-star-growthgit 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-github-star-growth)<a href="https://agentmods.dev/skills/gingiris-1031/gingiris-skills/gingiris-github-star-growth"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/gingiris-github-star-growth.svg" alt="Measured on agentmods" 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 | $0.00357 | $0.03307 |
| Opus 5 | $0.00179 | $0.01654 |
| Sonnet 5 | $0.00071 | $0.00661 |
| Haiku 4.5 | $0.00036 | $0.00331 |
Grade A, and why
gingiris-github-star-growth 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 5d 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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Star 持续增长实操手册
⚠️ Scope Differentiation:
gingiris-opensource= Launch burst (0→1K in 72h). This skill = Sustained monthly growth (1K→10K→60K+).
Star 地区分布 & 发布时序
适用于 launch 阶段（和
gingiris-opensource配合使用）
- 同一国家/地区 star 占比不超过 20%，是上 GitHub Trending 的隐性门槛。
- 工具：star-history.com（涨幅趋势）· OSS Insight（国家分布，国人开发）
- 发布时序：第一周海外宣发，第二周才做国内（朋友圈 / 中文社群）。
- AFFiNE 真实案例：开源第一周拿 6000 star，几乎全来自海外渠道。
- 陪跑好的项目节奏参考：至少提前 2-3 个月准备； 爆发期一周 4000-6000 star，三周可达 10000 star。
Star 地域分布健康基准
AFFiNE 开源前一周研究 VSCode / Vue / AppFlowy 等知名开源项目 star 分布总结的基准（口述回忆，数字加 ≈）
| 地区 | 健康占比 |
|---|---|
| 美国 | ≈19–21% |
| 中国 | ≈19–21% |
| 欧洲（法德意英合计） | ≈10–15% |
| Top 10 常客 | 印度、俄罗斯、巴西通常在列 |
- 硬规则：开源第一周全员禁发朋友圈，内容英文优先——防止中文圈流量污染全球化分布。AFFiNE 第一周 ≈6,000 star 的分布与知名项目基准吻合（中国 ≈19%）。
- 反例教训：第二周团队发朋友圈后，中国占比飙到 ≈35%，此后再没降下来——分布一旦被污染基本不可逆。
- 反模式：呼朋唤友第一二天刷 500-1,000 star 是舒适圈行为，会污染"全球市场试水温"的真实信号。
README 用户路径闭环（Star CTA Loop）
AFFiNE 开源实战 SOP：任何用户路径跳出去，都要能跳回来点 star。
- 枚举用户所有出口路径：README→官网；README→开发者文档；README→社区（Discord 等）。
- 每个出口页面都反向链接回 GitHub README：官网、文档站、社区页全部回链。
- 每个回链位置加一句 star CTA（"If you like us, please give us a star on GitHub"）。
- 闭环自检：沿每条路径走一遍，确认用户从任意出口都能一步跳回 repo 并看到 star 引导。
- README/官网随阶段更新：策略目标转变（如转向商业化）就即时改版，不要一成不变。
Star 引流漏斗纪律
- 所有渠道统一回溯 GitHub link，而非官网：Reddit / Twitter / Medium / 目录站发布的链接终点都指向 repo，保证所有流量都会看到 README 里的 star 引导。
- README 30 秒内让用户 get 价值，并直接引导点 star。
- 首周每个渠道发不同主题的 post，不是同一篇复制粘贴。
- 起量第一周每天看 GitHub Insights 的流量来源，据此决定接下来几天重点维护哪些渠道；"维护"= 去该渠道和潜在用户对话，不是 paid marketing。
- 私信对标产品的 Twitter follower list：发布日定向私信 Notion / Miro 类竞品的 follower，AFFiNE 实战转化 ≈几百个 star（口述数字）。
- 战略转移信号：star 到 ≈6,000 后不再把精力放在 GitHub 本身，转向 1v1 用户聊天 + 搭建用户社区。
- 永远不要买 star：AFFiNE 从未买星，star 涨太快时多家投资人 DD 写爬虫验证 star 来源，完全干净——买星在尽调时必被识破。
- 看增速不看绝对值：star 增速（而非总量）才是融资和 Trending 信号；GitHub Trending 有账号权限体系，非唯 star 论。
核心目标
| 指标 | 月度目标 | 数据来源 |
|---|---|---|
| 月增 Star | 300+ | Client Project A 8个月辅导数据 |
| 内容产出 | 8-12篇/月 | 博客+视频+社交 |
| KOL 触达 | 60-80/月 | 每天3-4个 |
| 社区活动 | 1次线上/月 | 31-34人参与 |
| Contributor PR | 10+/月 | Good First Issues |
What ships with it
4 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.
- 5d ago First seen · 222 lines · 357 tokens per session scan A b4060a6dbdd4
gingiris-github-star-growth is a skill published in the GitHub repository Gingiris-1031/gingiris-skills (75 stars, last pushed yesterday), licensed MIT. It adds 357 tokens to every session and 3,307 once invoked, about $0.0018 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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