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 youtube-thumbnailgit 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/youtube-thumbnail)<a href="https://agentmods.dev/skills/charlie947/social-media-skills/youtube-thumbnail"><img src="https://agentmods.dev/badge/skills/charlie947/social-media-skills/youtube-thumbnail/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/youtube-thumbnail"><img src="https://agentmods.dev/badge/skills/charlie947/social-media-skills/youtube-thumbnail.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 | $0.00099 | $0.01243 |
| Opus 5 | $0.00049 | $0.00622 |
| Sonnet 5 | $0.00020 | $0.00249 |
| Haiku 4.5 | $0.00010 | $0.00124 |
Grade A, and why
youtube-thumbnail 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:
- youtube-thumbnail — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Thumbnail
CRITICAL: Auto-start on load
When this skill triggers, go straight to Step 1.
Step 1. Gather inputs
Check the project for a reference photo config. Look in this order:
thumbnail-config.mdin the project rootbrand-kit.md— look for a reference image path and brand coloursabout-me.md— for the creator's name and positioning
If a reference photo path is stored, pre-fill it. Otherwise ask:
Upload or provide the path to the reference photo of yourself you want used in the thumbnail. Ideally a clear headshot with distinctive lighting and expression you plan to reuse across videos for brand consistency.
Then call AskUserQuestion:
[
{
"question": "What is the video title?",
"header": "Title",
"multiSelect": false,
"options": [
{"label": "I will type the title", "description": "Type the full working title"},
{"label": "Suggest one", "description": "Given the topic, propose 3 click-worthy titles first"}
]
},
{
"question": "Emotional tone?",
"header": "Tone",
"multiSelect": false,
"options": [
{"label": "Shock / surprise", "description": "Wide eyes, open mouth, bold reaction"},
{"label": "Curious / thinking", "description": "Slight smirk, raised eyebrow, gaze off-frame"},
{"label": "Confident / direct", "description": "Eye contact, calm, assertive"},
{"label": "Frustrated / strong take", "description": "Intense gaze, hand gesture, tension"}
]
}
]
Step 2. Apply thumbnail best practices
Every thumbnail must follow these rules:
- Face fills 30 to 50 percent of the frame. Readable at small sizes.
- 3 to 5 words maximum of large text. 6 if absolutely necessary.
- Two colours dominate. Brand primary + one high-contrast accent (yellow, red, cyan work well).
- One clear focal element besides the face. Tool logo, bold number, arrow, or prop.
- High contrast between face, text, and background. Test by squinting.
- Text is not a sentence. It is a hook phrase. Examples: "I fired my team", "Claude can now...", "Don't do this".
- No small text, no logos bottom-right (watch time icon sits there).
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 · 133 lines · 99 tokens per session scan A 8a14346d43a0
youtube-thumbnail is a skill published in the GitHub repository charlie947/social-media-skills (3,385 stars, last pushed 13d ago), licensed MIT. It adds 99 tokens to every session and 1,243 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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