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 multi-agent-researchgit 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/multi-agent-research)<a href="https://agentmods.dev/skills/thaolst/ai-growth-agents-for-marketers/multi-agent-research"><img src="https://agentmods.dev/badge/skills/thaolst/ai-growth-agents-for-marketers/multi-agent-research/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/multi-agent-research"><img src="https://agentmods.dev/badge/skills/thaolst/ai-growth-agents-for-marketers/multi-agent-research.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 | $0.00086 | $0.00419 |
| Opus 5.5 | $0.00034 | $0.00168 |
| Sonnet 5.5 | $0.00017 | $0.00084 |
| Haiku 4.5 | $0.00009 | $0.00042 |
Grade A, and why
multi-agent-research 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 2mo 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
Multi-Agent Research & Planning Agent
Bạn hướng dẫn marketer chạy hai agent nối tiếp nhau — không cần code, chỉ cần hai cuộc trò chuyện riêng trong Claude.
Context check: Reads
.agents/product-marketing-context.mdfor market context. If growth-mcp connected, pulls market data before research phase.
Workflow
Agent 1: Research
Input: campaign data, target, budget. Output: structured JSON insights về market, segments, past performance, recommendations.
Agent 2: Strategy
Input: JSON từ Agent 1 + constraints. Output: executable campaign plan với timeline, budget, mechanics, risks.
Khi nào dùng
- Có nhiều dữ liệu campaign cần phân tích trước khi lập kế hoạch
- Muốn structured research notes (JSON) trước khi viết plan
- Campaign phức tạp, cần separation of concerns
Related Skills
- campaign-planning — tổng quan plan
- campaign-brief — chi tiết từng campaign
- growth-mcp-connect — pull research data
English
Guide the marketer through a two-agent sequential pipeline — no code required, just two separate conversations in Claude.
Agent 1 (Research) → Agent 2 (Strategy) = executable campaign plan.
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.
- 2mo ago First seen · 56 lines · 86 tokens per session scan A 3bffa3556b0f
multi-agent-research is a skill published in the GitHub repository thaolst/ai-growth-agents-for-marketers (5 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 419 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
audit
Analyze whether TikTok or Instagram search traffic is a viable growth channel for your business. Uses ScaleBrick's framework to evaluate demand, competition, content fit, and intent categories. Ends with a go/no-go recommendation.
competitors
Audit competitors using ScaleBrick's 3-surface framework (social, web/pages, SEO). Categorizes their pricing, features, and landing pages. Identifies gaps you can exploit, positioning angles no one is claiming, and specific moves you can make this week.
strategy
Generate a full marketing strategy using ScaleBrick's "TikTok as Search Engine" framework. Produces themes, pillars, voice, keyword plan, and posting schedule specific enough to execute on day one.
keywords
Research high-intent TikTok and Instagram search keywords using ScaleBrick's framework. Returns categorized keywords with intent type, search volume estimate, difficulty score, and content angle for each.
11-ai-growth
Prompt để tận dụng AI trong growth marketing. Từ personalization, segmentation, campaign optimization đến predictive analytics và gamification.
06-retention-strategy
Retention không phải là nhắc user quay lại. Là xây hệ thống để user tự muốn quay lại. Ở cấp S, retention dựa vào timing và ưu đãi đơn giản. Ở cấp M, có thể thêm layer gamification và segment-specific mechanic. Ở cấp L, xây loyalty program riêng.