Growth Lab is an open-source growth system that uses coding agents to understand a product, research markets, execute growth activities, and learn from the results. It is designed for teams that want to manage growth work across channels such as SEO and Xiaohongshu through natural-language collaboration, persistent product context, and recorded outcomes. Catalogue add-ons define parts of its product models, research methods, execution workflows, and agent operation.
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 tsingyuai/growth-lab --skill research-productgit clone --depth 1 https://github.com/tsingyuai/growth-labWrote 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/tsingyuai/growth-lab/research-product)<a href="https://agentmods.dev/skills/tsingyuai/growth-lab/research-product"><img src="https://agentmods.dev/badge/skills/tsingyuai/growth-lab/research-product/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/tsingyuai/growth-lab/research-product"><img src="https://agentmods.dev/badge/skills/tsingyuai/growth-lab/research-product.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00100 | $0.00797 |
| Opus 5 | $0.00050 | $0.00398 |
| Sonnet 5 | $0.00020 | $0.00159 |
| Haiku 4.5 | $0.00010 | $0.00080 |
Grade A, and why
research-product 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.
What it actually says
渐进式产品研究
目标不是完成一份产品报告，而是让 Agent 在真实执行中逐步认识产品。每次只研究当前任务需要的部分，并更新能够跨轮次复用的稳定认知。
证据层级
按以下顺序区分，不得混写：
- 已观察事实：代码、配置、路由、界面、公开页面、真实数据或用户明确陈述直接支持。
- 暂定解释：多条事实共同支持，但仍需要使用或市场证据验证。
- 工作假设：为了推进当前 loop 提出的可能用户、问题、场景或价值，必须写明验证方式。
- 未知：没有证据时保留“未知”，不为了填满 SOUL 而补齐。
代码通常只能直接证明产品形态、组成和可见能力。不得从“存在某功能”直接推断“用户最需要它”“它解决了某个核心问题”或“这是产品差异化”。
每次执行
- 读取现有
SOUL.md和当前 Model 的相关 Memory，确定这次真正缺少哪一小块产品认知。 - 定位产品载体：当前/相邻本地仓库、用户指定路径、原型、线上 URL 或用户描述。
- 只读检查与问题直接相关的代码、文档、路由、配置和页面，不进行无目标的全仓扫描。
- 需要验证可见行为时，让调用方 Model 使用 screenshot-assets 获取真实页面证据；截图和本次研究记录进入该 Model 的 Memory。
- 列出“新增事实 / 被修正事实 / 新假设 / 仍未知”，每项附来源路径、URL、截图或用户陈述。
- 按 SOUL 增量写入协议 修改
SOUL.md。只更新本轮有新证据的字段，不重写整份文件。 - 将带时间的检查过程、证据清单和下一步验证动作写入调用方 Model 的
memory/<model-name>/products/<product-slug>/。
写入边界
SOUL.md：产品的稳定认知、明确假设、关键未知和证据链接。- Model Memory：本轮检查过程、截图、页面状态、临时分析、冲突证据和后续验证任务。
- 产品仓库：产品实现本身；除非用户要求修改，不因研究而写入。
- Collector：只维护研究方法，不保存某个产品的事实。
发现冲突时保留旧说法和新证据，先降级为“待验证”，不要静默覆盖。用户明确纠正产品事实时，记录为用户陈述，并在能验证时补上产品证据。
交付
报告本轮实际研究了什么、SOUL 哪些行发生变化、哪些结论仍只是工作假设、证据保存在哪里，以及下一次应在什么真实行动中验证。不要宣称已经“完整理解产品”。
What ships with it
2 files 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.
- 11d ago First seen · 43 lines · 100 tokens per session scan A 8fdedc4f146b
research-product is a skill published in the GitHub repository tsingyuai/growth-lab (2,000 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 100 tokens to every session and 797 once invoked, about $0.0005 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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