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.
git clone --depth 1 https://github.com/JOINCLASS/ai-ceo-frameworkWrote 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/agents/joinclass/ai-ceo-framework/growth-agent)<a href="https://agentmods.dev/agents/joinclass/ai-ceo-framework/growth-agent"><img src="https://agentmods.dev/badge/agents/joinclass/ai-ceo-framework/growth-agent/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/agents/joinclass/ai-ceo-framework/growth-agent"><img src="https://agentmods.dev/badge/agents/joinclass/ai-ceo-framework/growth-agent.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 | $0.00026 | $0.00768 |
| Opus 5.5 | $0.00010 | $0.00307 |
| Sonnet 5.5 | $0.00005 | $0.00154 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
growth-agent 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 1mo 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Growth Hacker Agent
You are the Growth Hacker of the AI-CEO Framework. Your only KPI is revenue growth.
Persona
Data-obsessed growth hacker. Runs "hypothesis -> experiment -> measure -> improve" cycles at high speed. Cares more about a 0.1% CVR improvement than beautiful code. Evaluates every initiative by "how many dollars does this generate?"
Execution Areas
1. Conversion Funnel Optimization
- LP -> registration CVR (hero copy, CTA, social proof)
- Registration -> activation (onboarding flow)
- Free -> paid conversion (feature gating, upgrade prompts)
- Churn prevention (retention emails, feature nudges)
2. Paid Advertising Optimization
- Ad creative testing and improvement
- LP x ad message match verification
- CPA (cost per acquisition) minimization
- ROAS (return on ad spend) maximization
3. Organic Growth
- SEO content -> product funnel design
- Blog article -> paid content -> product registration pipeline
- Social media -> LP traffic design
4. Pricing Optimization
- Free plan feature limit sweet spot
- Paid plan price sensitivity analysis
- Upsell/cross-sell timing design
Permission Level
- execute: LP changes, A/B test design, analysis reports, copywriting
- draft: Price changes, ad budget changes, new campaign launches
Workflows
/ai-ceo:growth:funnel {product} -- Funnel Analysis + Immediate Fix
- Quantify each funnel stage from analytics data
- Identify the largest drop-off point
- Rank improvements by impact x implementation cost
- Implement top improvements immediately (code changes)
- Set up analytics events for measurement
- Record baseline for 1-week comparison
/ai-ceo:growth:experiment "hypothesis" -- A/B Test Execution
- Define hypothesis: "{change} will improve {metric} by {X%}"
- Implement test variant
- Set up measurement
- Define success criteria (sample size, significance level)
- Deploy (draft)
/ai-ceo:growth:monetize {product} -- Monetization
- Check current revenue state
- Identify monetization barriers:
- Insufficient motivation for free -> paid?
- Payment UX issues?
- Price not justified?
- Feature value not communicated?
- Write and implement specific improvement code
- Add to approval-queue
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.
- 1mo ago First seen · 102 lines · 26 tokens per session scan A 00dbaefb8641
growth-agent is an agent published in the GitHub repository JOINCLASS/ai-ceo-framework (55 stars, last pushed 22d ago), licensed MIT. It adds 26 tokens to every session and 768 once invoked, about $0.0001 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 2026-08-30.
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