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 motiful/product-shots --skill product-shots-ad-creativegit clone --depth 1 https://github.com/motiful/product-shotsWrote 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/motiful/product-shots/product-shots-ad-creative)<a href="https://agentmods.dev/skills/motiful/product-shots/product-shots-ad-creative"><img src="https://agentmods.dev/badge/skills/motiful/product-shots/product-shots-ad-creative/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/motiful/product-shots/product-shots-ad-creative"><img src="https://agentmods.dev/badge/skills/motiful/product-shots/product-shots-ad-creative.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00187 | $0.03799 |
| Opus 5.5 | $0.00075 | $0.01520 |
| Sonnet 5.5 | $0.00037 | $0.00760 |
| Haiku 4.5 | $0.00019 | $0.00380 |
Grade A, and why
product-shots-ad-creative 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 2mo 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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Creative Design
Designs ad creatives that are platform-native (correct dimensions, safe zones, text overlay policy, character limits), industry-precise (visual DNA per industry × ad objective), and prompt-sanitized (no platform names / UI artifacts / fake ad elements leak into the image generator). The skill lays out an 8-platform × 21-format matrix, a six-step workflow, a self-check gate, and a banned-words system with a positive-preservation companion rule.
Engagement Principles
These rules apply across every Section. Read before acting.
- No generic one-size-fits-all creatives — every creative is locked to a specific (platform, format, objective, industry) tuple. No tuple = no work.
- Hard Constraints first — Section 0 (Platform Dimensions / Safe Zones / Text Overlay / Ad Copy Char Limits) is loaded at Step 0 and re-validated at the Self-Check Gate before delivery. Violations like "text on Google Display" or "CTA in TikTok bottom 18%" cause platform takedowns or render the ad invisible.
- Brand Name & User Copy Fidelity is MUST-level — every brand name, slogan, CTA text, and price/offer the user specified must appear verbatim in the deliverable. Never paraphrase, translate, or substitute user-supplied copy.
- Industry × Objective drives composition + color — pick the composition pattern and color strategy from the cross-decision matrix (Section 8 / Section 9), not from generic style intuition.
- Platform Style Profiles are not stylistic suggestions — they are constraints. TikTok requires anti-ad aesthetic AND complete brand/product/CTA info; LinkedIn requires ≥60pt fonts; Pinterest requires aspirational scenes; Google Display requires zero text overlay.
- Prompt Banned Words are filtered out of the image generator prompt — platform names, ad-UI terms, brand-tool names never reach the model. They are used internally for routing and rule selection only.
- Required Positive Instruction protects native packaging text — "no text overlay" must not delete printed text on the product itself. Always pair the banned-words filter with the positive preservation instruction (references/banned-words.md §Required Positive Instruction).
- Match the user's language — respond in English if the user writes English, in Chinese if Chinese. Never switch language unprompted.
- Use
<suggestion>for option sets — when proposing platform / objective / industry / asset choices, wrap them in<suggestion>tags. Never ask multiple questions at once.
What ships with it
11 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.
- references/ad-objective-rules.md 5.2 KB
- references/banned-words.md 6.4 KB
- references/color-and-cta-strategy.md 8.4 KB
- references/composition-patterns.md 6.4 KB
- references/failures-defaults-output.md 7.3 KB
- references/hard-constraints.md 14 KB
- references/industry-style-rules.md 7.1 KB
- references/multi-platform-output.md 6.0 KB
- references/platform-style-profiles.md 7.5 KB
- references/quick-start-and-fidelity.md 6.2 KB
- references/workflow-and-self-check.md 11 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.
- 2mo ago First seen · 236 lines · 187 tokens per session scan A 0ca48ef8ec94
product-shots-ad-creative is a skill published in the GitHub repository motiful/product-shots (129 stars, last pushed 5mo ago), licensed MIT. It adds 187 tokens to every session and 3,799 once invoked, about $0.0007 per session on Opus 5.5. 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.
product-shots-ad-creative compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| product-shots-ad-creativemotiful/product-shots | Skill | 129 | 5mo ago | MIT | A | 187 | Claude Code, Codex |
| magazine-web-pptnexu-io/open-design | Skill | 106k | yesterday | Apache-2.0 | A | 67 | Claude Code, Codex |
| kami-decknexu-io/open-design | Skill | 106k | yesterday | Apache-2.0 | A | 49 | Claude Code, Codex |
| orbit-notionnexu-io/open-design | Skill | 106k | yesterday | Apache-2.0 | A | 117 | Claude Code, Codex |
| fs-editorial-forestnexu-io/open-design | Skill | 106k | yesterday | Apache-2.0 | A | 44 | Claude Code, Codex |
| hps-bauhausnexu-io/open-design | Skill | 106k | yesterday | Apache-2.0 | A | 45 | Claude Code, Codex |
| html-ppt-zhangzara-coralnexu-io/open-design | Skill | 106k | yesterday | Apache-2.0 | A | 55 | Claude Code, Codex |