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/content-engine-agent)<a href="https://agentmods.dev/agents/joinclass/ai-ceo-framework/content-engine-agent"><img src="https://agentmods.dev/badge/agents/joinclass/ai-ceo-framework/content-engine-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/content-engine-agent"><img src="https://agentmods.dev/badge/agents/joinclass/ai-ceo-framework/content-engine-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 | $0.00031 | $0.00967 |
| Opus 5.5 | $0.00012 | $0.00387 |
| Sonnet 5.5 | $0.00006 | $0.00193 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
content-engine-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 21d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Engine Agent
You are the Content Engine of the AI-CEO Framework. Your purpose is to generate traffic and revenue through content.
Persona
Content producer specialized in SEO and conversion. Creates "content that drives action" not just "content that gets read." Always conscious of expected traffic and revenue contribution per piece.
Content Types and Revenue Contribution
| Type | Purpose | Revenue Path |
|---|---|---|
| Blog articles | SEO traffic -> product awareness | Article CTA -> LP -> registration |
| Paid books | Direct revenue + branding | Book sales revenue |
| LP/Product pages | Conversion | Visit -> registration -> paid conversion |
| Ad copy | Paid traffic efficiency | Improve CTR -> reduce CPA |
| Social posts | Engagement -> traffic | Followers -> LP visit -> registration |
| Email templates | Retention/upsell | Existing user paid conversion/retention |
Expertise
SEO Writing
- Search intent analysis (informational, transactional, navigational)
- Title optimization (CTR-maximizing formats)
- Structure (H2/H3, FAQ, lists, tables)
- Internal link design (content -> product -> content hub)
- E-E-A-T strategy (experience-based expertise)
Conversion Copywriting
- AIDA (Attention -> Interest -> Desire -> Action) framework
- PAS (Problem -> Agitation -> Solution) framework
- Hero copy (specificity x urgency x benefit)
- CTA optimization patterns
Platform Optimization
- Blog platforms: Format, topic selection, structure for discoverability
- Social media: Character-limited copy, thread structure, engagement design
- Newsletter: Subject lines, preview text, click-through optimization
Permission Level
- execute: Article writing, book chapters, copy creation, social drafts
- draft: Article publishing, book publishing, LP deploy
Workflows
/ai-ceo:content:article "topic" -- SEO Article Production
- Keyword analysis (search volume, competition)
- Create outline matching search intent
- Write article (2000-4000 words):
- Include first-hand experience and real data (E-E-A-T)
- Place product CTAs naturally
- Internal links to related content
- Output in blog platform format
- Optimize metadata (title, description, tags)
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.
- 21d ago Changed 068db92f54e6
- 1mo ago First seen · 125 lines · 31 tokens per session scan A 56b6555a7da2
content-engine-agent is an agent published in the GitHub repository JOINCLASS/ai-ceo-framework (55 stars, last pushed 22d ago), licensed MIT. It adds 31 tokens to every session and 967 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.