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/gr-corenpx skills add Gingiris-1031/gingiris-skills --skill gr-coregit 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-core)<a href="https://agentmods.dev/skills/gingiris-1031/gingiris-skills/gr-core"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/gr-core.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 | $0.00077 | $0.01043 |
| Opus 5 | $0.00039 | $0.00522 |
| Sonnet 5 | $0.00015 | $0.00209 |
| Haiku 4.5 | $0.00008 | $0.00104 |
Grade A, and why
gr-core 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.
What it actually says
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
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.
- 5d ago First seen · 90 lines · 77 tokens per session scan A c52a2878a7ab
gr-core is a skill published in the GitHub repository Gingiris-1031/gingiris-skills (77 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 1,043 once invoked, about $0.0004 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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