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 killvxk/pm-skills-zh --skill growth-loopsgit clone --depth 1 https://github.com/killvxk/pm-skills-zhWrote 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/killvxk/pm-skills-zh/growth-loops)<a href="https://agentmods.dev/skills/killvxk/pm-skills-zh/growth-loops"><img src="https://agentmods.dev/badge/skills/killvxk/pm-skills-zh/growth-loops/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/killvxk/pm-skills-zh/growth-loops"><img src="https://agentmods.dev/badge/skills/killvxk/pm-skills-zh/growth-loops.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 | $0.00079 | $0.01501 |
| Opus 5 | $0.00039 | $0.00750 |
| Sonnet 5 | $0.00016 | $0.00300 |
| Haiku 4.5 | $0.00008 | $0.00150 |
Grade A, and why
growth-loops 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 11d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
增长循环
概述
识别并设计创造可持续增长牵引力的增长循环（Growth Loops）。本技能评估五种已验证的增长循环机制，帮助降低对付费获客的依赖，构建产品驱动增长能力。
适用场景
- 为产品设计增长机制
- 构建可持续的病毒式或转介绍增长
- 降低对付费获客的依赖
- 分析竞争对手的增长策略
- 为产品驱动增长（PLG）优化产品体验
五种增长循环类型
1. 病毒式飞轮（Viral Loop）
用户创作的内容在外部平台传播，将新用户带回产品。
- 机制：用户在产品内创作内容 → 分享到社交/外部平台 → 新用户发现并注册
- 典型案例：以链接形式分享的 Figma 设计文件、通过邮件传播的 Loom 视频
- 优势：若内容天然具有传播力，可实现指数级用户增长
- 挑战：需要高度可分享的输出内容，以及强烈的分享动机
2. 使用飞轮（Usage Loop）
用户在产品内创作内容或创造价值，通过分享吸引新用户或促进老用户回归。
- 机制：用户创作 → 分享作品 → 他人消费 → 成为活跃用户
- 典型案例：Twitter 长推文、Medium 文章、公开分享的 Notion 模板
- 优势：增长与产品使用和网络效应直接挂钩
- 挑战：需要将内容创作的摩擦降到极低
3. 协作飞轮（Collaboration Loop）
用户邀请同事在产品内共同协作，在组织内部扩大用户基础。
- 机制：用户创作 → 邀请同事协作 → 同事发现产品价值
- 典型案例：Google Docs 邀请协作、Figma 团队项目、Slack 频道
- 优势：深度渗透组织内部，留存率极高
- 挑战：最适合协作或团队型产品
4. 用户生成内容飞轮（UGC Loop）
用户通过消费他人的创作发现新内容或功能，进而创作并分享自己的内容。
- 机制：用户发现内容 → 创作类似内容 → 分享作品 → 他人发现
- 典型案例：TikTok、Pinterest、YouTube 趋势驱动创作者参与
- 优势：形成内容飞轮和网络效应
- 挑战：需要足够数量的优质内容才能持续运转
5. 转介绍飞轮（Referral Loop）
用户为获取奖励、激励或社交认可，邀请其他潜在用户加入。
- 机制：用户转介绍 → 被邀请用户加入 → 转介绍者获得奖励 → 继续邀请他人
- 典型案例：Dropbox 转介绍奖励、Uber 乘客邀请、PayPal 注册奖励
- 优势：直接激励获客，ROI 易于衡量
- 挑战：需要有吸引力的激励，且不能侵蚀单位经济模型
工作方式
第一步：定义产品价值
明确用户获得的核心价值：
- 用户在产品中执行的主要操作
- 每次用户操作创造的价值
- 是否存在网络效应（如有）
- 体验中的摩擦点
第二步：评估飞轮适配性
评估哪些增长循环与你的产品相匹配：
- 产品类型（协作型、内容型、工具型等）
- 目标用户行为和分享习惯
- 已有的网络效应
- 现有用户基础和活跃度
第三步：设计飞轮机制
制定具体的飞轮实施方案：
- 触发分享或邀请的动作
- 参与激励（内在动机或外在激励）
- 分享机制的便捷程度
- 邀请到激活的转化率
- 单个用户的飞轮循环频率
第四步：计算飞轮系数
估算增长速度：
- 每个用户每次循环的邀请数/分享数
- 邀请转化为新用户的转化率
- 每次循环净新增用户数
- 每次循环的时间周期
第五步：构建飞轮
优先实施杠杆最大的飞轮：
- 从最契合产品的飞轮开始
- 优化信息传达和减少摩擦
- 追踪飞轮指标和转化率
- 随时间复利增长
输入格式
通过 $ARGUMENTS 传入：
- 产品描述和用户主要操作
- 目标用户画像和行为特征
- 现有的分享/协作功能
- 当前增长渠道和数据指标
- 约束条件或机会点
输出内容
增长循环分析报告，包含：
- 五种飞轮类型针对你产品的排序评估
- 推荐的主要增长循环及实施计划
- 后续可叠加的辅助飞轮
- 核心指标和衡量框架
- 30-60-90 天实施路线图
- 潜在飞轮系数和增长预测
方法论
基于 Ognjen Bošković 的增长循环研究。聚焦于通过产品原生的分享和协作机制，实现用户获取的复利增长。
实用技巧
- 先专注掌握一个飞轮，再增加复杂度
- 病毒式飞轮增速最快，但需要时间搭建
- 协作飞轮能创造最强的留存和 LTV（用户生命周期价值）
- 优化阶段每周衡量飞轮健康度
- 达到规模后叠加多个飞轮，产生乘数效应
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.
- 11d ago First seen · 126 lines · 79 tokens per session scan A 8e1ccbdffa60
growth-loops is a skill published in the GitHub repository killvxk/pm-skills-zh (159 stars, last pushed 5mo ago), licensed MIT. It adds 79 tokens to every session and 1,501 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…