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 run-wechat-article-loopgit 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/run-wechat-article-loop)<a href="https://agentmods.dev/skills/tsingyuai/growth-lab/run-wechat-article-loop"><img src="https://agentmods.dev/badge/skills/tsingyuai/growth-lab/run-wechat-article-loop/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/run-wechat-article-loop"><img src="https://agentmods.dev/badge/skills/tsingyuai/growth-lab/run-wechat-article-loop.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 | $0.00093 | $0.01237 |
| Opus 5.5 | $0.00037 | $0.00495 |
| Sonnet 5.5 | $0.00019 | $0.00247 |
| Haiku 4.5 | $0.00009 | $0.00124 |
Grade A, and why
run-wechat-article-loop 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 18d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
run-wechat-article-loop — 公众号长文闭环
这条闭环的目标
- 打开（阅读数）= 标题与首屏钩子：让目标读者在订阅列表或转发卡片里想点开。
- 读完与转发（完读、分享、在看）= 完整解释 + 用户价值：一篇只解决一个主问题，每节都给读者可带走的结果。
- 转化（阅读原文、产品入口点击）= 产品能力服务于方法：产品出现在读者需要它的那一步，并有具体 CTA。
诚实边界：公众号打开量主要受订阅基数、推送时段和转发链路影响，单篇内容优化够不到这些结构性上限。没有粉丝基础的新号，先把文章当作可被转发、可被搜一搜收录的长期资产，而不是期望单次推送带量。
依赖缺失时触发统一的 onboard-growth-lab。本 Skill 不自行维护 onboarding。
① 观察与选题 → ② 创作 → ③ 预览与检查 → ④ 草稿同步 → ⑤ 受控发布 → ⑥ 结果回收与复盘
① 观察与选题
- 读取
SOUL.md；产品事实不足以支撑本篇时调用 research-product，只把已确认的增量写回SOUL.md。 - 读取
memory/run-wechat-article-loop/中近期文章、发布状态和复盘，避免重复选题，并沿用已被数据支持的标题/结构规律。 - 选题证据可来自：本产品在其他渠道已验证的内容（如
memory/xhs-replicate/的高表现笔记、memory/run-seo-page-loop/中有展现的查询）、用户反馈、产品更新，或通过 media-crawler 等 Collector 采集的公开需求证据。 - 产出一句话选题：读者问题 + 本篇给出的方法 + 产品在哪一步出现 + 1-2 篇复刻锚。复刻锚由用户确认。
② 创作
按 wechat-article-compose 执行，生产单元放在 memory/run-wechat-article-loop/outputs/<YYYYMMDD-slug>/。source-review.md 经用户确认后才写正文。
③ 预览与检查
按 wechat-mp-publish 第 1 步运行 make wechat-preview 与 make wechat-render，用真实浏览器查看 preview.html；make wechat-lint 未通过时回到 ② 修改。
④ 草稿同步（默认终点）
按 wechat-mp-publish 第 2 步创建草稿，本机直连或远程服务二选一。缺少凭据、IP 白名单或接口权限时，交付完整发布包（preview.html、article.html、cover.png、assets/、wechat.yml 中的标题与摘要）走人类协作发布。
⑤ 受控发布
只有用户明确要求自动发布，且三重确认齐备时才执行 wechat-mp-publish 第 3 步。否则停在草稿，请用户在后台发布后回传文章 URL 与发布时间，写入 publish-state.json。
⑥ 结果回收与复盘
用户说“看看公众号数据”“这批复盘一下”或主动提供数据时，执行 结果回收与复盘。不自动触发回收，不自动修改方法。
保留的人工反馈点
- 选题与复刻锚确认。
source-review.md确认。- 公众号后台草稿预览。
- 是否发布（
publish.approved只由用户改）。
不做
- 不产出小红书卡片，不复用小红书站外导流规则。
- 不批量发布、不删除已发布文章、不跳过草稿预览。
- 不让 AI 伪造产品 UI，不写
SOUL.md之外无法验证的产品能力。
Growth Lab Memory 接入
- 每篇文章的生产单元（brief、正文、配置、自检、预览、
publish-state.json）放在memory/run-wechat-article-loop/outputs/<YYYYMMDD-slug>/。 - 发布后的阅读与转化数据按月写入
memory/run-wechat-article-loop/publish-log/YYYY-MM.md。 - 复盘结论、下一步选题建议写入带日期的 Memory 文件，并链接对应生产单元。
- 方法改进直接修改本 Model 或对应 Executor，不写进 Memory。
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.
- 18d ago First seen · 68 lines · 93 tokens per session scan A e1654e93b2c6
run-wechat-article-loop is a skill published in the GitHub repository tsingyuai/growth-lab (1,992 stars, last pushed 19d ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,237 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-29.
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