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/gingiris-skills --skill gr-b2b-growthgit clone --depth 1 https://github.com/Gingiris/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/gingiris-skills/gr-b2b-growth)<a href="https://agentmods.dev/skills/gingiris/gingiris-skills/gr-b2b-growth"><img src="https://agentmods.dev/badge/skills/gingiris/gingiris-skills/gr-b2b-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 5.1 | $0.00083 | $0.00922 |
| Opus 5 | $0.00042 | $0.00461 |
| Sonnet 5 | $0.00017 | $0.00184 |
| Haiku 4.5 | $0.00008 | $0.00092 |
Grade A, and why
gr-b2b-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 8d 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-b2b-growth — B2B SaaS 增长
什么时候用
- "我的 B2B SaaS 怎么从 0 做到 PMF"
- "PLG 还是 SLG，我该选哪个"
- "怎么做 affiliate 营销"
- "渠道合作 / 集成伙伴怎么谈"
- "Enterprise sales 怎么起"
增长飞轮（4 象限）
PLG（产品驱动）
↑
|
已有流量 + 短决策 ←→ 需要信任 + 长周期
|
↓
SLG（销售驱动）
选型判据：
- ACV < $1k/年 & 决策周期 < 1 周 → PLG（Vercel、Supabase 模式）
- ACV > $10k/年 & 决策周期 > 1 个月 → SLG（Snowflake、Databricks 模式）
- 中间 → PLG-to-SLG hybrid（HeyGen、Deel 模式）
5 阶段路径
| 阶段 | ARR | 关键动作 |
|---|---|---|
| Pre-PMF | $0-$100k | 10-30 次用户访谈（联动 gr-user-interview），确认 must-have |
| Early PMF | $100k-$1M | 投 1-2 个渠道做深（不要广撒网） |
| Growth | $1M-$5M | 引入 affiliate + 集成伙伴，PLG 加 sales-assist |
| Scale | $5M-$10M | Enterprise 团队、合规认证（SOC2）、channel 加码 |
| Ecosystem | $10M+ | 开放平台、investor / agency 分销 |
深度参考
📂 https://github.com/Gingiris/gingiris-b2b-growth
references/foundation.md— PMF 验证 + 定价分层references/contracts.md— 合同/条款模板（SaaS 标准条款 + 本地化）references/seo-geo-guide.md— B2B SEO 飞轮references/en/— 英文版
级联推荐
- Pre-PMF 阶段需要访谈 →
gr-user-interview - PMF 后要做内容 →
gr-blog-post（含 B2B 文风：数据 > 故事） - 出海需要本地化 →
gr-aso或gr-blog-posti18n - 找对标案例 →
gr-competitor
B2B 内容特殊要求
- 必须有 case study（ROI / 节约时间 / 节约人力）
- 必须有可验证数据（"our customer X saved Y hours/month"）
- 必须有 CFO 友好的 pricing 页（ROI 计算器、年付折扣、合规文档）
- ❌ 不要用 ToC 营销话术（"amazing"、"magical"）
反模式
- ❌ 没 PMF 就投流（烧钱速度比学习速度快 10x）
- ❌ PLG 做到 $1M 就盲目转 SLG（很多死在这里 —— Vercel 是反例，坚持 PLG 到 $100M）
- ❌ 只看 ARR 不看 NRR（NRR < 100% = 漏水桶）
- ❌ Enterprise 只给一个合同模板（要分 startup / mid-market / enterprise 三版）
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.
- 8d ago First seen · 91 lines · 83 tokens per session scan A b8c3d4a681e5
gr-b2b-growth is a skill published in the GitHub repository Gingiris/gingiris-skills (23 stars, last pushed 3mo ago), licensed MIT. It adds 83 tokens to every session and 922 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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