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-seo-page-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-seo-page-loop)<a href="https://agentmods.dev/skills/tsingyuai/growth-lab/run-seo-page-loop"><img src="https://agentmods.dev/badge/skills/tsingyuai/growth-lab/run-seo-page-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-seo-page-loop"><img src="https://agentmods.dev/badge/skills/tsingyuai/growth-lab/run-seo-page-loop.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 | $0.00065 | $0.00956 |
| Opus 5 | $0.00032 | $0.00478 |
| Sonnet 5 | $0.00013 | $0.00191 |
| Haiku 4.5 | $0.00006 | $0.00096 |
Grade A, and why
run-seo-page-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 12d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run the SEO page loop
Coordinate the loop inside the product workspace. Let the current Codex or Claude Code session control the work. Use memory/run-seo-page-loop/ as this Model's persistent Memory.
Read Memory → Observe → Decide → Act → Review → Write Memory → Next observation
Read memory.md before starting. Recover relevant observations, actions, outcomes, conclusions, and next-action recommendations.
Boundaries
- Keep this Model focused on when and why the loop moves between observation, decision, action, and review.
- Delegate data-collection methods and source-specific interpretation to Collectors.
- Delegate creation, implementation, publishing, inspection, and performance-review techniques to Executors.
- Use Runtime-native browser, search, page inspection, screenshot, and local web-testing capabilities directly.
- Add a Client only for an external API action the Runtime cannot perform natively.
- Create no fixed schema, database, dashboard, workflow state, or task queue.
- Store dated operational evidence, analysis, outcomes, and next-action recommendations in Memory.
- Apply improvements to the loop itself directly to this Model. Keep methodology-change suggestions out of Memory.
1. Read Memory
Read recent Memory entries and older entries relevant to the product, page, query family, or pending action. Establish what is already known, what was attempted, what happened, and which recommendation should now be tested.
2. Observe
Invoke $research-seo-demand to collect and interpret current search demand and live SERP evidence. Combine it with product context and relevant Memory.
When the loop begins from an existing page, invoke $review-seo-performance first to observe its current outcome.
Persist useful raw evidence and a dated observation in memory/run-seo-page-loop/.
3. Decide
Before choosing a page action, confirm that the current observation contains a competitor-page breakdown for every candidate query being considered. The breakdown must cover three to five relevant leading pages and include:
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.
- 12d ago First seen · 81 lines · 65 tokens per session scan A 5e216a77c9d3
run-seo-page-loop is a skill published in the GitHub repository tsingyuai/growth-lab (1,998 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 65 tokens to every session and 956 once invoked, about $0.0003 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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