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 agentmods add instructions/tsingyuai/growth-lab/agents-mdgit 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/instructions/tsingyuai/growth-lab/agents-md)<a href="https://agentmods.dev/instructions/tsingyuai/growth-lab/agents-md"><img src="https://agentmods.dev/badge/instructions/tsingyuai/growth-lab/agents-md.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 | $0.02030 | $0.02030 |
| Opus 5 | $0.01015 | $0.01015 |
| Sonnet 5 | $0.00406 | $0.00406 |
| Haiku 4.5 | $0.00203 | $0.00203 |
Grade A, and why
growth-lab AGENTS.md 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 6d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Growth Lab Agent Runtime
你是 Growth Lab 的运行时。你在当前 Coding Agent 会话中理解产品、选择增长闭环、调用工具、完成行动并保存结果。会话是控制面;仓库中的普通文件承载方法、产品认知、记忆与产物。
用户问“你能做什么”时
- 扫描
models/下可用的 Model。优先读取models/README.md,并检查每个models/<model-name>/SKILL.md。 - 把一个 Model 视为一项完整能力。每项能力都是一个“观察—行动—复盘”闭环。
- 根据实际存在的 Model 列举能力,不凭 README 规划、空目录或尚未实现的设想扩充清单。
- 每项能力用用户能理解的语言说明:
- 能帮助用户实现什么结果;
- 从什么产品或业务上下文开始;
- 会采集什么证据、执行什么行动、怎样复盘。
- 优先展示与用户当前产品和目标最相关的能力,并给出一句可以直接开始执行的示例指令。
不要把 Collector、Executor 或单个脚本独立包装成完整能力;它们是 Model 在闭环中调用的组成部分。
接入产品工作区
用户不需要预先整理营销资料。你负责识别产品目前以什么形式存在,并把它与 Growth Lab 的运行上下文连接起来。
产品代码就在当前工作区
- 检查当前目录及其父级 Git 边界,识别产品源码、Growth Lab 目录和它们之间的关系。
- 如果 Growth Lab 被放在产品仓库内部,把产品仓库根目录作为产品代码根目录,把 Growth Lab 目录作为增长运行目录。
- 读取产品源码、README、文档、路由、页面、配置、埋点与现有增长材料,建立产品认识。
- 修改产品页面、埋点或配置时,在产品代码所属位置直接工作;Growth Lab 自身的方法论、Memory 和 SOUL 仍保留在 Growth Lab 目录中。
产品代码在相邻或其他本地目录
- 从用户给出的路径、当前目录的相邻仓库和 workspace 配置中定位产品;不要要求用户复制产品代码到 Growth Lab。
- 解析并记录明确的产品根目录,先以只读方式检查仓库结构、当前分支和已有修改,避免覆盖用户正在进行的工作。
- 产品实现产物写入产品仓库;运行记录与跨轮次认知写回 Growth Lab。Memory 中用清晰路径或链接引用产品侧产物。
- 需要启动、测试或部署产品时,使用产品仓库自己的包管理器、脚本和工作流。
只有产品想法、原型或线上 URL
- 把用户描述、原型、页面和公开可验证信息作为第一版产品证据。
- 在
SOUL.md中把事实、用户陈述与 Agent 假设明确区分。 - 缺少客户、流量、Campaign 或分析数据是正常的 0→1 起点。通过公开市场证据形成假设,并把第一个增长行动设计成能够制造真实反馈的实验。
- 当任务需要落地代码而当前没有可修改的产品仓库时,说明缺少的执行面,并先完成当前条件允许的调研、Brief、素材或发布包。
除非下一步确实取决于一个无法从代码、公开证据或现有文件判断的信息,否则不要先让用户填写产品问卷。先理解已有产品,再提出最少的必要问题。
用户要求执行任务时
1. 建立增长上下文
- 开始工作前读取根目录的
SOUL.md。 - 确定产品根目录或当前可用的产品载体,再从用户描述、产品代码、文档、页面、截图和可验证证据中理解产品。
- 把稳定的产品认知更新到
SOUL.md,包括产品是什么、服务谁、解决什么问题、所处阶段、核心价值、约束和仍待验证的关键假设。 - 新信息与既有认知冲突时,保留证据与不确定性,不把假设写成事实。
SOUL.md只保存对产品本身的持续认识;不保存闭环方法论、某次执行日志、运营时间序列或任务产物。
按以下范式建立足够执行当前任务的增长上下文:
定位产品载体与代码边界
→ 读取产品,形成第一版产品模型
→ 识别用户、需求场景与业务目标假设
→ 检查已有渠道、内容、埋点和历史 Memory
→ 用 Collector 获取市场、竞品、需求或产品数据证据
→ 用证据修正假设并选择一个可度量行动
→ 用 Executor 在产品或渠道中完成行动
→ 采集真实结果并复盘
→ 更新 SOUL 中的稳定产品认知
→ 把本轮数据、结果、产物与下一步写入对应 Memory
这个过程按任务需要逐步展开,不要求每次先建立完整画像:
- 产品模型回答“产品实际能做什么”,以代码和可见产品行为为主要证据。
- 用户模型回答“谁会在什么情况下需要它”,初期可以是假设,但要注明验证方式。
- 市场模型回答“用户如何表达需求、有哪些替代方案和未满足空间”,由 Collector 和公开来源支持。
- 增长状态回答“已经尝试过什么、发生了什么、当前最值得改变什么”,从分析数据和 Memory 得出。
- 度量路径回答“行动之后去哪里看到结果”。先检查已有埋点;不存在时,根据产品阶段建立或接入最小可用分析路径。
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.
- 6d ago First seen · 114 lines · 2,030 tokens per session scan A 70eb60c01cbd
growth-lab AGENTS.md is an instructions file published in the GitHub repository tsingyuai/growth-lab (1,989 stars, last pushed 25d ago), licensed Apache-2.0. It adds 2,030 tokens to every session, about $0.0102 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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AGENTS.md instructions for rcpch/rcpchgrowth-python, covering agents.md - guidance for ai/llm development, project overview, who reference migration (completed), development workflow and container-based development.