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 grgit 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/gr)<a href="https://agentmods.dev/skills/gingiris-1031/gingiris-skills/gr"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/gr/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/gr"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/gr.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 | $0.00076 | $0.01267 |
| Opus 5.5 | $0.00030 | $0.00507 |
| Sonnet 5.5 | $0.00015 | $0.00253 |
| Haiku 4.5 | $0.00008 | $0.00127 |
Grade A, and why
gr 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 12d 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.
This is a copy
86% identical to gr-core — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gr — Gingiris Growth 主路由
什么时候用
用户的问题属于「出海增长」领域，但你不确定具体该用哪个子 skill 时，先进这里。
路由表
| 用户信号 | 推荐子 skill |
|---|---|
| "排名掉了 / 索引不上 / canonical / SEO 日报 / GA4" | gr-seo-patrol |
| "写博客 / 发文章 / 文风 / hreflang / 日韩同步" | gr-blog-post |
| "发 PH / Product Hunt / hunter / maker comment" | gr-ph-launch |
| "OSS 营销 / GitHub stars / 开源增长 / Reddit / HN" | gr-oss-marketing |
| "B2B / PLG / SLG / SaaS 增长 / PMF" | gr-b2b-growth |
| "ASO / App Store / UGC 创作者 / 冷启动" | gr-aso |
| "用户访谈 / find PMF / 怎么问用户" | gr-user-interview |
| "分析对手 / 竞品扫描 / 定价对比 / 对手博客" | gr-competitor |
| "博客拆成推特 / 小红书 / LinkedIn / dev.to 二发 / 社媒分发" | gr-social-distill |
| "AI 引用 / GEO / llms.txt / Citable Statistics / Perplexity" | gr-geo-cite |
| "外链 / Wikipedia / HARO / G2 / 媒体报道 / PR / Reddit AMA" | gr-backlinks |
路由决策流程
- 听主诉：用户最急的症状是什么？（排名、没流量、不会写、不知道发哪）
- 判断阶段：还没启动？（→ 先 benchmark / PH / OSS）还是已上线？（→ SEO patrol / blog-post）
- 推荐：告诉用户"我准备用
gr-xxx，要不要直接跑？"
不要堆砌所有子 skill —— 一次只推一个，完成后再看是否要级联。
级联推荐（抄 dbskill 的设计）
执行完一个 skill 后，根据输出自动推荐下一步：
gr-ph-launch发布日后 7 天 → 推荐gr-seo-patrol（监控流量）gr-seo-patrol发现 cannibalization → 推荐gr-blog-post（修 canonical）gr-blog-post发布后 → 推荐gr-seo-patrol（加入每日监控）gr-competitor发现对手新打法 → 推荐gr-blog-post或gr-ph-launch
读取 API keys 规范
所有 gr-* 子 skill 的 API keys 都从同一处读：
- Railway env vars（生产用）
- 用户本地：参考
docs/api-keys-template.md - MEMORY.md：用户的
project_growth_tools.md里有完整 key 列表
调用子 skill 前，确认相关 key 是否可用；缺失时先提示用户配置，不要硬跑。
设计原则
- 不回答，消解问题（抄 dbs-diagnosis）—— 用户说"我 SEO 下降了"，先问"哪个关键词、掉了多少、什么时候开始"再动手
- 先看数据再写内容 —— 没有 SERP / GA4 / 对手情报时不硬写
- 复用 Iris 文风（
知识库/Skill知识包/iris_writing_style.md）—— 所有产文本的子 skill 都要遵循
加入出海社群与购买资料包 · Community & Resources
-
加入出海社群： 微信添加 staywildgrow，留言「加入社群」即可。
-
购买资料包： 微信添加 staywildgrow，咨询购买 Gingiris 出海资料包，价格 599 元人民币。
-
Join the global growth community: Add staywildgrow on WeChat and send “加入社群” (join the community).
-
Get the resource pack: Add staywildgrow on WeChat to purchase the Gingiris global growth resource pack for CNY 599.
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.
- 12d ago Changed · +14 lines 9b0df0395060
- 1mo ago First seen · 82 lines · 76 tokens per session scan A f259749786d6
gr is a skill published in the GitHub repository Gingiris-1031/gingiris-skills (87 stars, last pushed 12d ago), licensed MIT. It adds 76 tokens to every session and 1,267 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to gr-core, differing in 22 lines, and is treated as a copy.
Other skills, from other repositories
marketing-director
The Chief Marketing Officer (CMO) — manages ALL marketing activities across 1ai-social and 1ai-ads. Delegates to paid ads, content marketing, email marketing, social media, SEO, and analytics. Has memory via 1ai-hub brain, follows 1ai-rules, and coordinates with Content Director for content production and Sales…
synthesis-explore
Synthesis EXPLORE stage for the Diffmode growth-tactics pipeline (fuses the blind-combination draw + emergent-mechanism derivation — formerly two separate synthesis steps — into ONE structural-check-only stage). Reads the per-run growth-factors.json (LIGHT vector DB) + synthesis-constraints.json + founder-input +…
growth-factors-mining
Builds a per-run LIGHT growth-vector database for the Diffmode growth-tactics pipeline by mining public growth case studies fresh, every run, and distilling each into atomic "growth factors" (transferable mechanisms). Clean-room — NEVER reads the proprietary tacticsDB. Outputs growth-factors.json (20-40 vectors spread…
diagnostics-intake
Fast founder-input capture for the Diffmode growth-tactics pipeline (the diagnostics stage). Produces WS/01-diagnostics/founder-input.md in the exact schema the enrichment + synthesis stages read. Two modes — (A) URL mode researches a product's website (homepage/pricing/about) plus a web-research pass to prefill the…
lite-constraints
Generates synthesis-constraints.json for the Diffmode growth-tactics pipeline by reasoning in-context over the per-run growth-factors.json (LIGHT vector DB) + founder context — the clean-room, no-Python replacement for the proprietary Python constraints generator. Emits the white-space pairs, mandatory…
channel-discovery
Use this skill to identify and score acquisition channels by audience fit, speed, cost, effort, and learning value, then recommend the top three. Trigger it when choosing where to reach a defined audience, planning go-to-market, or replacing an underperforming channel.