charlie947/social-media-skills is a collection of markdown instructions that gives AI agents specialized workflows for creating and managing social-media content. It supports a content system spanning LinkedIn, Instagram, Substack, X, and YouTube, with shared voice and context files guiding the individual skills.
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 charlie947/social-media-skills --skill gemini-carouselgit clone --depth 1 https://github.com/charlie947/social-media-skillsWrote 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/charlie947/social-media-skills/gemini-carousel)<a href="https://agentmods.dev/skills/charlie947/social-media-skills/gemini-carousel"><img src="https://agentmods.dev/badge/skills/charlie947/social-media-skills/gemini-carousel/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/charlie947/social-media-skills/gemini-carousel"><img src="https://agentmods.dev/badge/skills/charlie947/social-media-skills/gemini-carousel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00092 | $0.00976 |
| Opus 5 | $0.00046 | $0.00488 |
| Sonnet 5 | $0.00018 | $0.00195 |
| Haiku 4.5 | $0.00009 | $0.00098 |
Grade A, and why
gemini-carousel 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 13d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- gemini-carousel — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemini Carousel
CRITICAL: Auto-start on load
When this skill triggers, go straight to Step 1. Do not summarise.
Step 1. Gather inputs
Ask:
Paste the content you want in the carousel. A post, section of a newsletter, research notes, or a framework all work.
Wait for the content, then call AskUserQuestion:
[
{
"question": "Brand style?",
"header": "Style",
"multiSelect": false,
"options": [
{"label": "Pull from brand-kit.md", "description": "Use the colours and typography in my project brand file"},
{"label": "I will type brand colours", "description": "I will paste hex codes and font preferences"},
{"label": "Suggest for me", "description": "Pick a palette and typography based on the content"}
]
},
{
"question": "Number of slides?",
"header": "Slides",
"multiSelect": false,
"options": [
{"label": "6 slides", "description": "Concise, fast read"},
{"label": "8 slides", "description": "Standard carousel length"},
{"label": "10 slides", "description": "Deep-dive carousel"}
]
}
]
Step 2. Build the design brief
Analyse the content and produce a slide-by-slide brief with:
- Slide 1 (Cover): hook, large bold text, visual direction
- Slides 2 to N-1 (Body): one idea per slide, max 15 words per slide, visual suggestion
- Slide N (CTA): repost ask, name, link or offer
For each slide include:
- Slide number
- Headline (max 8 words)
- Body text (max 15 words)
- Visual suggestion (icon, colour block, illustration, diagram)
Tell the user:
Here is the design brief. Tell me what to change, or say "generate" when you are happy.
Wait for approval. Do not proceed until the user explicitly approves.
Step 3. Output per-slide prompts
Once approved, output one Gemini image generation prompt per slide, each in its own code block, numbered clearly.
Every prompt follows this structure:
Act as an expert graphic designer. Create a LinkedIn carousel slide at 1080x1350 pixels (4:5 aspect ratio).
Brand style:
- Primary colour: [HEX]
- Secondary colour: [HEX]
- Accent colour: [HEX]
- Typography: [bold industrial headline font, clean geometric body font]
- Aesthetic: modern, authoritative, high contrast
Slide [N of M]: [slide purpose]
Content:
- Headline: "[headline text]"
- Body: "[body text]"
- Visual element: [specific visual suggestion]
Layout instructions:
- [Headline placement and size]
- [Body placement and size]
- [Visual placement]
- [Background treatment]
Constraints:
- Vertical 4:5 aspect ratio at exactly 1080x1350 pixels
- No watermarks, no logos unless specified above
- Maintain visual consistency with the other slides in the set
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.
- 13d ago First seen · 122 lines · 92 tokens per session scan A cc0bf2a52d41
gemini-carousel is a skill published in the GitHub repository charlie947/social-media-skills (3,385 stars, last pushed 13d ago), licensed MIT. It adds 92 tokens to every session and 976 once invoked, about $0.0005 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 2026-08-30.
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