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 xiaohongshu-mcpgit 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/xiaohongshu-mcp)<a href="https://agentmods.dev/skills/tsingyuai/growth-lab/xiaohongshu-mcp"><img src="https://agentmods.dev/badge/skills/tsingyuai/growth-lab/xiaohongshu-mcp/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/xiaohongshu-mcp"><img src="https://agentmods.dev/badge/skills/tsingyuai/growth-lab/xiaohongshu-mcp.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 | $0.00078 | $0.00753 |
| Opus 5 | $0.00039 | $0.00377 |
| Sonnet 5 | $0.00016 | $0.00151 |
| Haiku 4.5 | $0.00008 | $0.00075 |
Grade A, and why
xiaohongshu-mcp 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Xiaohongshu browser-first collection
Read runtime.md before startup and cover-screening.md before visual selection.
First-run conversation
Before collection, tell the user:
- the recommended first-run batch is 25 notes;
- the count is adjustable, but 25 is recommended;
- collection is read-only and saves sanitized research evidence and requested images locally;
- login does not authorize likes, saves, comments, follows, uploads, or publication.
If required settings are missing, stop and invoke onboard-growth-lab. Give the user the exact configuration file and fields from CONFIGURATION.md; never ask them to paste a key, cookie, or signed URL into the conversation.
Runtime
powershell -ExecutionPolicy Bypass -File collectors/xiaohongshu-mcp/scripts/start_xiaohongshu_service.ps1
python collectors/xiaohongshu-mcp/scripts/collect_xiaohongshu.py "<topic>" `
--limit 25 --cover-pool 25 `
--out "memory/xhs-replicate/<run>/xiaohongshu-search.json"
The service must be local HTTP only. If it is not logged in, explain the boundary, ask before opening the visible login window, run login_xiaohongshu.ps1, verify once, and resume. Stop on timeout, risk-control, login loss, or repeated empty responses; do not loop around platform controls.
Visual selection
- Persist the 20-30 item search response immediately as one batch. Do not wait for page-wide stability after the response is complete.
- Download all covers from the first batch and inspect every contact sheet.
- Score promotional layout quality before engagement. Fetch full details only for 3-8 passing candidates.
- Show every passing candidate with its actual representative image, title, score, and risk. If inline image rendering is unavailable or cannot be confirmed, include the clean public note URL in the same response.
- If the user rejects all candidates, record the reasons and run a new product/workflow-oriented query. Do not force the best item from a weak batch.
- Select exactly one external visual learning sample. Write and validate
visual-reference-selection.json:
What ships with it
11 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 257 B
- references/cover-screening.md 3.4 KB
- references/runtime.md 1.6 KB
- scripts/collect_selected_xiaohongshu_note.py 3.4 KB runs code
- scripts/collect_xiaohongshu.py 21 KB runs code
- scripts/login_xiaohongshu.ps1 1.6 KB runs code
- scripts/start_xiaohongshu_service.ps1 3.7 KB runs code
- scripts/test_collect_xiaohongshu.py 8.2 KB runs code
- scripts/test_minimal_continuous_flow.py 6.1 KB runs code
- scripts/test_validate_visual_reference_selection.py 2.5 KB runs code
- scripts/validate_visual_reference_selection.py 3.1 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.
- 12d ago First seen · 56 lines · 78 tokens per session scan A 516fa2fd1855
xiaohongshu-mcp is a skill published in the GitHub repository tsingyuai/growth-lab (1,998 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 78 tokens to every session and 753 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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