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 alecs5am/ralphy --skill ugc-ad-productiongit clone --depth 1 https://github.com/alecs5am/ralphyWrote 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/alecs5am/ralphy/ugc-ad-production)<a href="https://agentmods.dev/skills/alecs5am/ralphy/ugc-ad-production"><img src="https://agentmods.dev/badge/skills/alecs5am/ralphy/ugc-ad-production/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/alecs5am/ralphy/ugc-ad-production"><img src="https://agentmods.dev/badge/skills/alecs5am/ralphy/ugc-ad-production.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 | $0.00033 | $0.01552 |
| Opus 5 | $0.00016 | $0.00776 |
| Sonnet 5 | $0.00007 | $0.00310 |
| Haiku 4.5 | $0.00003 | $0.00155 |
Grade A, and why
ugc-ad-production 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 7d 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.
UGC Ad Production Pipeline
Required Inputs (ask the user for ALL of these before starting)
Before generating anything, collect:
- Product — what is being advertised (name, URL, or image)
- Reference UGC video — a real UGC video in the same niche/style (Pinterest, TikTok, YouTube link). Used for script style, creator personality, and shot reference.
- Creator face references — 2+ images of real people (attractive, matching the brand vibe). These get face-mixed in Nano Banana Pro to create a new, non-existent creator.
- Target audience — who the ad is for (e.g. women 18-35 with acne-prone skin)
- Hook type — problem/solution, before/after, testimonial, transformation, or let the agent decide based on the product
- NEVER ask
- Which editing tool to use (always Canva)
- Video length (always 15 seconds)
- Aspect ratio (always 9:16 vertical)
- Which video model (always Kling 3.0)
- Which image model (always Nano Banana Pro)
- Step 1 — Script Writing (Claude 4.6)
- Tool: Claude claude-sonnet-4-6 (strong reasoning + creative writing — feed it as much context as possible)
Prompt template:
Make a UGC script for a 15-second video like [REFERENCE VIDEO] but for [PRODUCT]. Use [CREATOR DESCRIPTION from face references] as the UGC creator/speaker. Include:
- Hook (first 3-5 seconds): show the problem visually + audio hook
- Voiceover lines with exact words
- Cut descriptions: what the camera shows at each moment (creator face, product, before/after, etc.)
- Actions and mannerisms: make the creator feel like a real character — nervous laugh, hair tuck, direct eye contact, pointing at product, etc.
- Call to action (last 2-3 seconds)
- Timestamps for each cut/action Output as a shooting script with columns: Timestamp | Voiceover | Visual/Shot | Action/Mannerism Notes:
Give Claude maximum context: the reference video URL, product details, target audience pain points, other viral UGC videos in the niche, storytelling frameworks (problem-agitate-solve, before/after, etc.) The more context, the better the script. Claude's reasoning capability is the advantage here. Virality principles to bake in: pattern interrupt hook, social proof, transformation moment, urgency CTA Output: A timestamped shooting script with voiceover, visuals, and creator mannerisms.
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.
- 7d ago First seen · 125 lines · 33 tokens per session scan A d7f6a6cae255
ugc-ad-production is a skill published in the GitHub repository alecs5am/ralphy (133 stars, last pushed yesterday), licensed Apache-2.0. It adds 33 tokens to every session and 1,552 once invoked, about $0.0002 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 2026-09-03.
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