No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by tsingyuai · Codex Skill · ★ 1.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is growth-lab safe to install? View the security audit →
🧪 Growth Lab 用自然语言运行产品运营和用户增长闭环：理解产品，发现真实需求，采取行动，再从结果中找到下一步。 官网 简体中文 · English · 繁體中文 · Français · Español · Русский · Português · Deutsch · 日本語 · 한국어 · Türkçe · Tiếng Việt · Polski 为什么做 Growth Lab 大多数 AI 增长工具只解决一个局部问题：有的生成文案，有的研究竞品，有的负责发布，有的展示数据。产品上下文在不同工具之间反复丢失，真正重要的决策仍散落在仪表盘、文档、Prompt 和人工交接中。 增长是一个持续学习的闭环： 现代 Coding Agent 已经能够读取代码仓库、搜索网络、操作工具、编辑文件、跨上下文推理，并在会话中与人协作。Growth Lab 以 Codex 和 Claude Code 作为应用 Runtime，将这些能力组织成端到端的增长工作流。 产品模型 Skill 教 Runtime 如何理解产品、开展研究、选择工具、形成策略、制作内容、分析反馈和决定下一步。Client 工具让 Runtime 能够使用浏览器、官方 API、数据导出、内容平台和素材生成等外部能力。文件跨会话保存产品上下文、研
| Stars | 1,058 |
| Forks | 90 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 66.5155155699442/100 |
| Last Updated | 2026-08-11 |
| Created | 2026-07-24 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~14k |
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growth-lab is An end-to-end growth tool that understands the product, fetch the data it needs, researches the market, executes campaigns, and reviews results to improve the next round of growth. 从代码到市场的开源端到端增长工具。理解. It is categorized as a Codex Skill with 1.1k GitHub stars.
growth-lab is primarily written in Python. It covers topics such as agentic-marketing, ai-agent, claude-code.
You can find installation instructions and usage details in the growth-lab GitHub repository at github.com/tsingyuai/growth-lab. The project has 1.1k stars and 90 forks, indicating an active community.
growth-lab is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to growth-lab on Agent Skills Hub include ai-marketing-claude, claude-blog, digital-marketing-pro. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: