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 realjaymes/marketingagentskills --skill ai-content-generationgit clone --depth 1 https://github.com/realjaymes/marketingagentskillsWrote 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/realjaymes/marketingagentskills/ai-content-generation)<a href="https://agentmods.dev/skills/realjaymes/marketingagentskills/ai-content-generation"><img src="https://agentmods.dev/badge/skills/realjaymes/marketingagentskills/ai-content-generation/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/realjaymes/marketingagentskills/ai-content-generation"><img src="https://agentmods.dev/badge/skills/realjaymes/marketingagentskills/ai-content-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00186 | $0.02590 |
| Opus 5 | $0.00093 | $0.01295 |
| Sonnet 5 | $0.00037 | $0.00518 |
| Haiku 4.5 | $0.00019 | $0.00259 |
Grade A, and why
ai-content-generation 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 10d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Content Generation
Help the user actually make AI image and video content, end to end, with copy-paste prompts at every step. This skill covers four use cases, each a self-contained, do-this-then-that playbook. The hard parts (keeping a character consistent and killing the AI-slop look) are baked into the prompts, so the user can follow the steps without prior experience.
The core idea
The model is the easy part. What makes AI content win is the niche, the script, keeping the character or product consistent, and beating the AI-slop look. Every playbook bakes those in. Roughly 80% of quality is set before the model runs (research, script, consistency, realism); the model is the last 20%.
Pick the use case, then open its reference
| The user wants to... | Use this reference |
|---|---|
| Run a recurring AI character on TikTok or Instagram, no face on camera | references/faceless-influencer.md |
| Clone themselves into a talking head and generate videos from scripts | references/ai-clone-talking-head.md |
| Make paid ad creative at volume (UGC, product video, static) | references/ai-ad-creative.md |
| Build a faceless YouTube channel (long-form and shorts) | references/faceless-youtube.md |
Each reference is fully self-contained: a numbered walkthrough split into one-time setup and a repeating loop, with the exact prompts inline.
How to run this skill
- Identify which of the four use cases the user is after (ask one question if unclear).
- Open the matching reference file and follow it with the user, step by step.
- At each tool step, give the user the copy-paste prompt from the reference, filled in with their specifics (niche, topic, product, audience).
- Keep one character, one voice, and one look consistent across a user's content. Consistency is the most common failure point.
- Apply the realism rules below to every generation prompt (they are already written into the reference prompts; do not strip them out).
- Before the user publishes, run the reference's pre-publish checklist.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/chatgpt-project/knowledge/00-overview.md 11 KB
- assets/chatgpt-project/knowledge/01-faceless-influencer.md 11 KB
- assets/chatgpt-project/knowledge/02-ai-clone-talking-head.md 20 KB
- assets/chatgpt-project/knowledge/03-ai-ad-creative.md 23 KB
- assets/chatgpt-project/knowledge/04-faceless-youtube.md 20 KB
- assets/chatgpt-project/project-instructions.md 4.5 KB
- references/ai-ad-creative.md 23 KB
- references/ai-clone-talking-head.md 20 KB
- references/faceless-influencer.md 11 KB
- references/faceless-youtube.md 20 KB
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.
- 10d ago First seen · 126 lines · 186 tokens per session scan A a910950190c1
ai-content-generation is a skill published in the GitHub repository realjaymes/marketingagentskills (59 stars, last pushed today), licensed MIT. It adds 186 tokens to every session and 2,590 once invoked, about $0.0009 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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