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 xhs-render-cardsgit 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/xhs-render-cards)<a href="https://agentmods.dev/skills/tsingyuai/growth-lab/xhs-render-cards"><img src="https://agentmods.dev/badge/skills/tsingyuai/growth-lab/xhs-render-cards/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/xhs-render-cards"><img src="https://agentmods.dev/badge/skills/tsingyuai/growth-lab/xhs-render-cards.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 | $0.00089 | $0.00918 |
| Opus 5 | $0.00044 | $0.00459 |
| Sonnet 5 | $0.00018 | $0.00184 |
| Haiku 4.5 | $0.00009 | $0.00092 |
Grade A, and why
xhs-render-cards 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 10d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Xiaohongshu card production
Read visual-production-contract.md, rendering.md, and visual-review-rubric.md. Keep DAI and compliance rules from deai.md and quality-gates.md.
Required inputs
Require before rendering:
- approved draft, exact card copy, account role, reader, and one-sentence user value;
- verified Product facts and Product-owned screenshots or brand assets;
- one validated
visual-reference-selection.jsonwhen external visual research is used; - output directory in the calling
memory/xhs-replicate/run; - target canvas, card count, rights/privacy boundaries, and whether paid image generation is approved.
The selected public reference is analysis-only. Learn hierarchy, proof-zone proportion, density, and reading rhythm; never copy its wording, logo, proprietary UI, exact composition, distinctive decoration, or full sequence. Rejected candidates contribute no visual rules.
1. Lock copy and evidence
Run DAI on title, caption, card copy, CTA, and tags. Every Product claim needs a current evidence source. Delete unsupported claims instead of weakening them with defensive filler.
Write image-plan.md using image-plan.md. Lock every visible string before rendering. Do not add cards merely to match the reference.
2. Capture real Product evidence
Use screenshot-assets for real Product UI, website, code, or data. Keep the original screenshot as evidence. Do not ask an image model to recreate Product UI, logos, citations, statistics, or source text and present it as real.
3. Choose one production mode
deterministic: HTML/CSS, Canvas, SVG, or Pillow owns layout and exact text. Preferred for Chinese copy, dense diagrams, real screenshots, and repeatable series.separable-layer: the image model creates only a background, texture, or illustration without text/UI; deterministic rendering places approved copy and Product evidence.complete-effect: the image model proposes a whole visual direction. Generated text, UI, logos, evidence, and sensitive claims must be replaced or the candidate rejected.
What ships with it
14 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.
- agents/openai.yaml 268 B
- references/deai.md 3.5 KB
- references/image-plan.md 2.2 KB
- references/imagegen-style-content.md 1.3 KB
- references/imagegen-style-cover.md 1.2 KB
- references/process-doc.md 1.2 KB
- references/quality-gates.md 2.1 KB
- references/rendering.md 2.5 KB
- references/visual-production-contract.md 3.4 KB
- references/visual-review-rubric.md 2.7 KB
- scripts/check-banned-phrases.py 9.0 KB runs code
- scripts/check-compliance.py 4.4 KB runs code
- scripts/test_validate_social_card_pack.py 2.8 KB runs code
- scripts/validate_social_card_pack.py 5.9 KB runs code
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.
- 10d ago First seen · 65 lines · 89 tokens per session scan A 87160b65d744
xhs-render-cards is a skill published in the GitHub repository tsingyuai/growth-lab (1,999 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 89 tokens to every session and 918 once invoked, about $0.0004 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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