Borrowing it
Nothing to install: this file belongs to florafauna-ai/flora-mcp-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/florafauna-ai/flora-mcp-skills/main/.agents/skills/flora-pdp-deck/SKILL.mdgit clone --depth 1 https://github.com/florafauna-ai/flora-mcp-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/florafauna-ai/flora-mcp-skills/flora-pdp-deck)<a href="https://agentmods.dev/skills/florafauna-ai/flora-mcp-skills/flora-pdp-deck"><img src="https://agentmods.dev/badge/skills/florafauna-ai/flora-mcp-skills/flora-pdp-deck/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/florafauna-ai/flora-mcp-skills/flora-pdp-deck"><img src="https://agentmods.dev/badge/skills/florafauna-ai/flora-mcp-skills/flora-pdp-deck.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 336 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00147 | $0.06792 |
| Opus 5 | $0.00073 | $0.03396 |
| Sonnet 5 | $0.00029 | $0.01358 |
| Haiku 4.5 | $0.00015 | $0.00679 |
Grade A, and why
flora-pdp-deck scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
runs API over curl needs the API key. Media urls do not. How it starts
The opening of the file, as written. The whole thing — 547 lines — stays where its author put it; the contents beside it link to each section on GitHub.
flora-pdp-deck
Self-contained. The deck builder is at the foot of this file; write it out and run it.
What it is
Not an image generator. A consistency engine. The product exists; the job is putting it in front of a camera many times without it drifting between frames.
The product is reference-locked. The world and the pose are generated. A seam moving, a strap ending somewhere new, a colour drifting — that is a fail.
What you can get from what you have
Start here. What the user hands you decides what you can honestly promise, and the limits are not obvious.
one product photo 1 clean plate + N on-model or context frames + a deck
+ a model frame the same, with a real person holding or wearing it
several real photographs one plate per photograph, plus the above
A single reference cannot be rotated by asking. The SHOT: line changes framing;
it does not turn the product. Measured — four plates from one photo, byte-identical
prompt block, only the shot line varying:
mean absolute grey difference, 0 = identical
three-quarter vs rear 180 deg 3.21 <- opposite faces requested
front vs three-quarter 6.39
each plate vs the reference 8.06 - 8.74
The plates differed from each other less than each differed from its own source. Four requested angles, four near-duplicates. On an asymmetric product it shows directly: a wax drip specified on one side stayed on that side in the frame asked to show the opposite face.
So from one photo: generate one plate, not four. Then spend the budget on on-model and context frames, where the composition genuinely changes and the model has real work to do. Say this to the user before firing. Four billed duplicates is the expensive way to learn it.
Intake — the image is in the chat, not in FLORA
The user drops a photo into the conversation. It is not in FLORA. Nothing generates until you put it there, and this is the step that stalls a run.
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 · 547 lines · 147 tokens per session scan A 83a3508b8208
flora-pdp-deck is a skill published in the GitHub repository florafauna-ai/flora-mcp-skills (8 stars, last pushed today), licensed MIT. It adds 147 tokens to every session and 6,792 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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