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 Livus-AI/Skills-MCP --skill content-flywheel-orchestratorgit clone --depth 1 https://github.com/Livus-AI/Skills-MCPWrote 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/livus-ai/skills-mcp/content-flywheel-orchestrator)<a href="https://agentmods.dev/skills/livus-ai/skills-mcp/content-flywheel-orchestrator"><img src="https://agentmods.dev/badge/skills/livus-ai/skills-mcp/content-flywheel-orchestrator/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/livus-ai/skills-mcp/content-flywheel-orchestrator"><img src="https://agentmods.dev/badge/skills/livus-ai/skills-mcp/content-flywheel-orchestrator.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 | $0.00058 | $0.01012 |
| Opus 5.5 | $0.00023 | $0.00405 |
| Sonnet 5.5 | $0.00012 | $0.00202 |
| Haiku 4.5 | $0.00006 | $0.00101 |
Grade A, and why
content-flywheel-orchestrator 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.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Flywheel Orchestrator
Execute the complete content flywheel workflow: research a topic and generate content for Twitter, LinkedIn, and newsletter in one session.
Workflow Overview
┌─────────────────────────────────────────────────────────────┐
│ 1. RESEARCH (content-research) │
│ └──> Output: Research Brief │
│ │ │
│ ▼ │
│ 2. TWITTER (twitter-thread-generator) │
│ └──> Output: 8-Tweet Thread │
│ │ │
│ ├────────────────────┐ │
│ ▼ ▼ │
│ 3. LINKEDIN 4. NEWSLETTER │
│ (linkedin-post-adapter) (newsletter-expander) │
│ └──> LinkedIn Post └──> Newsletter Issue │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ READY TO PUBLISH │ │
│ └─────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
Required Inputs
| Input | Description | Example |
|---|---|---|
| Topic | Subject to research and create content about | "Latest developments in AI agents" |
| Niche | Industry/audience context | "AI/Tech for developers and founders" |
| Newsletter Link | URL for CTAs | "https://newsletter.example.com" |
| Subscriber Count | For social proof (optional) | "10,000+" |
| Author Name | For newsletter sign-off | "Alex" |
Execution Steps
Step 1: Research Phase
Load and execute content-research skill:
- Research the topic using web search
- Identify 3-5 key developments
- Find surprising statistics
- Extract core narrative
- Define actionable takeaway
- Output: Research Brief
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 · 150 lines · 58 tokens per session scan A b44d0b2d5b43
content-flywheel-orchestrator is a skill published in the GitHub repository Livus-AI/Skills-MCP (2 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 1,012 once invoked, about $0.0002 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.
content-flywheel-orchestrator compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| content-flywheel-orchestratorLivus-AI/Skills-MCP | Skill | 2 | 5mo ago | MIT | A | 58 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| chat-pet-sprite-creationmicrosoft/vscode | Skill | 195k | today | MIT | A | 53 | Claude Code, Codex |