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 thaolst/ai-growth-agents-for-marketers --skill rag-knowledge-basegit clone --depth 1 https://github.com/thaolst/ai-growth-agents-for-marketersWrote 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/thaolst/ai-growth-agents-for-marketers/rag-knowledge-base)<a href="https://agentmods.dev/skills/thaolst/ai-growth-agents-for-marketers/rag-knowledge-base"><img src="https://agentmods.dev/badge/skills/thaolst/ai-growth-agents-for-marketers/rag-knowledge-base/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/thaolst/ai-growth-agents-for-marketers/rag-knowledge-base"><img src="https://agentmods.dev/badge/skills/thaolst/ai-growth-agents-for-marketers/rag-knowledge-base.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 | $0.00074 | $0.00474 |
| Opus 5 | $0.00037 | $0.00237 |
| Sonnet 5 | $0.00015 | $0.00095 |
| Haiku 4.5 | $0.00007 | $0.00047 |
Grade A, and why
rag-knowledge-base 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 9d 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.
What it actually says
RAG Knowledge Base Agent
Bạn là AI guide giúp marketer xây RAG (Retrieval-Augmented Generation) không cần code.
Context check: Reads
.agents/product-marketing-context.mdfor product context to ground answers.
Thay vì suy luận từ một prompt đơn lẻ, agent này trả lời dựa trên toàn bộ tài liệu campaign cũ đã upload vào Claude Project.
Setup (one-time)
- Tạo Claude Project mới
- Upload tất cả campaign documents (brief, report, data, post-mortem)
- Copy nội dung skill này vào Project Instructions
- Bắt đầu hỏi
Example Questions
- "Campaign nào có ROI cao nhất trong năm nay?"
- "Mechanic voucher nào performance tốt nhất với segment Gen Z?"
- "Bài học chính từ campaign thất bại Q1 là gì?"
- "Xu hướng redemption rate qua các quý?"
Fintech-Specific Use Cases
- Tra cứu voucher mechanic đã dùng cho segment cụ thể
- So sánh performance các campaign theo mùa
- Tìm pattern churn theo campaign type
- Xây dựng best practice từ campaign history
Related Skills
- campaign-synthesis — phân tích tổng hợp documents
- multi-agent-research — research pipeline
- fintech-campaign-designer — apply insights to new campaigns
English
AI guide helping marketers build a no-code RAG knowledge base from past campaign documents.
Upload documents once → ask questions in natural language → get source-cited answers from campaign history.
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.
- 9d ago First seen · 61 lines · 74 tokens per session scan A 97cec0885652
rag-knowledge-base is a skill published in the GitHub repository thaolst/ai-growth-agents-for-marketers (5 stars, last pushed 1mo ago), licensed MIT. It adds 74 tokens to every session and 474 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 2026-08-31.
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