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 palmier-io/palmier-skills --skill ugc-photo-promptsgit clone --depth 1 https://github.com/palmier-io/palmier-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/palmier-io/palmier-skills/ugc-photo-prompts)<a href="https://agentmods.dev/skills/palmier-io/palmier-skills/ugc-photo-prompts"><img src="https://agentmods.dev/badge/skills/palmier-io/palmier-skills/ugc-photo-prompts/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/palmier-io/palmier-skills/ugc-photo-prompts"><img src="https://agentmods.dev/badge/skills/palmier-io/palmier-skills/ugc-photo-prompts.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 | $0.00127 | $0.03372 |
| Opus 5 | $0.00063 | $0.01686 |
| Sonnet 5 | $0.00025 | $0.00674 |
| Haiku 4.5 | $0.00013 | $0.00337 |
Grade A, and why
ugc-photo-prompts 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 12d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UGC-Style Photo Prompts
Core principle
Image models default to polish: symmetric features, smooth retouched skin, studio lighting, clean backgrounds. That polish is the single biggest tell that an image is AI-generated, and it's exactly what kills performance for UGC ads and influencer content — the entire point of UGC is that it doesn't look produced.
Telling the model "make it look realistic" doesn't work, because "realistic" isn't a style the model can target — it's an absence of all the polish defaults, and the model needs that absence written into every slot of the prompt as a specific physical detail. Realism comes from specificity (a named flaw, a named light source, a named texture), not from the word "realistic" itself.
The 9-slot formula
Fill every slot, in this order, for every prompt:
- Format declaration — camera + shot type + aspect ratio, stated first. This locks the model into "photo," not "illustration." e.g. "Ultra-realistic iPhone front-camera selfie, 4:5 vertical" / "Ultra-photorealistic flash photo, 4:5 vertical."
- Framing & lens behavior — distance (arm's length / chest-up / full body), angle (slightly above or below eye level), and the lens distortion that comes with it (wide-angle selfie warp, a shoulder cropped out of frame, imperfect centering).
- Subject — the imperfection stack. Never write "beautiful woman, smiling." Specify hair texture and disorder (damp, windblown, flyaways), skin (tone + pores + one specific flaw — freckles, redness, sweat, under-eye shadow, tan line), and a momentary, specific expression (mid-laugh, looking off to the side, mouth slightly open) instead of a generic one.
- Wardrobe + accessories, named specifically. Fabric, fit, and at least one small accessory (hoops, a layered necklace, a bag strap). Generic clothing description reads as generic AI output — specificity here is doing real work, not just flavor.
- Pose as interaction, not posture. Give the subject something to physically do — hold an object, mid-gesture, walking, getting their hair touched by someone off-frame — rather than "posing for camera." Static posing is the second-biggest AI tell after skin polish.
- Setting with texture and clutter. Name actual materials (brick, stone, fridge glass, car upholstery, a tiled floor) and at least one incidental detail unrelated to the subject (a second person's arm in frame, a parked car, a visible price tag). Staged sets don't have incidental detail; real environments always do.
- Lighting, named and physical. State the actual light source (direct flash, golden-hour side light, fluorescent overhead, window light) and what it visibly does to skin and shadow — not "good lighting," but what the light is doing.
- Vibe tag. One short phrase tying the scene together — "everyday building-corridor look," "candid basement-party energy" — this keeps the model from sliding back into editorial polish even after all the specific detail above.
- Negative space. Say what NOT to do. This is doing as much work as the rest of the prompt — see the standing list below.
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.
- 12d ago First seen · 112 lines · 127 tokens per session scan A 8e2ecc25e6e4
ugc-photo-prompts is a skill published in the GitHub repository palmier-io/palmier-skills (66 stars, last pushed 11d ago), licensed Apache-2.0. It adds 127 tokens to every session and 3,372 once invoked, about $0.0006 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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