Borrowing it
Nothing to install: this file belongs to MohamedAbdallah-14/prompt-to-asset. 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/MohamedAbdallah-14/prompt-to-asset/main/.claude/skills/t2i-prompt-dialect/SKILL.mdgit clone --depth 1 https://github.com/MohamedAbdallah-14/prompt-to-assetWrote 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/mohamedabdallah-14/prompt-to-asset/t2i-prompt-dialect)<a href="https://agentmods.dev/skills/mohamedabdallah-14/prompt-to-asset/t2i-prompt-dialect"><img src="https://agentmods.dev/badge/skills/mohamedabdallah-14/prompt-to-asset/t2i-prompt-dialect/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/mohamedabdallah-14/prompt-to-asset/t2i-prompt-dialect"><img src="https://agentmods.dev/badge/skills/mohamedabdallah-14/prompt-to-asset/t2i-prompt-dialect.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 | $0.00095 | $0.03010 |
| Opus 5 | $0.00048 | $0.01505 |
| Sonnet 5 | $0.00019 | $0.00602 |
| Haiku 4.5 | $0.00010 | $0.00301 |
Grade A, and why
t2i-prompt-dialect 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
T2I prompt dialect
Runs between asset_enhance_prompt() and asset_generate_*(). Takes a normalized brief plus a target model and emits a prompt that honors that model's syntax, token budget, and quirks.
Per-model rules (quick card, verified Apr 2026)
| Model | Grammar | Token / char ceiling | Negative prompt | Transparency | Text ceiling |
|---|---|---|---|---|---|
gpt-image-2 |
Prose | ~100 words | silently ignored | NOT supported — background:"transparent" 400s (regression vs 1.5) — route transparent jobs to gpt-image-1.5 instead |
~80 chars / paragraph (~99% accuracy per third-party tests) |
gpt-image-1.5 |
Prose | ~100 words | silently ignored | API param background: "transparent" |
dense text ~60 chars (LM Arena #1) |
gpt-image-1 |
Prose | ~100 words | silently ignored | API param background: "transparent" |
~50 chars / ≤5 words |
| Imagen 4 (Fast/Standard/Ultra) | Narrative prose, ≥30 words suppresses auto-rewriter | unbounded | negativePrompt accepted on Vertex AI; ignored on Gemini API |
never request in prompt (renders checkerboard as RGB) | ≤25 chars per Google's own guidance |
| Gemini 3.1 Flash Image (Nano Banana 2) | Prose | unbounded | ignored | renders checkerboard | ~50 chars / ~90% accuracy (#1 Image Arena at launch — strong-text, not weak) |
| Gemini 3 Pro Image (Nano Banana Pro) | Prose | unbounded | ignored | renders checkerboard | paragraph-length reliable (~94-96% accuracy) |
| SD 1.5 / SDXL | Tag-soup, comma-separated, no articles | 77 CLIP tokens, chunk via BREAK |
native | LayerDiffuse adapter or matte post | "cannot render legible text" per HF card — ≤1 glyph |
| SD 3 / SD 3.5 Large | Prose + tags hybrid | 256 (T5) | native | matte post | ~30 chars (mid-tier; not strong-text) |
| Flux 1.1 Pro / Flux Pro | Prose narrative | 512 | rejected at fal schema; no-op on BFL native | matte post | 1–3 words / ~25 chars |
| Flux 2 (pro/flex/dev/klein) | Prose, JSON-structured prompts allowed | T5 + Mistral-3 24B VLM | officially unsupported per BFL guide ("FLUX.2 does not support negative prompts") | matte post | 5–10 words / one tagline (~92% layout accuracy) |
| Flux Schnell | Prose | 256 | unsupported | matte post | 1–2 words max |
| Midjourney v7/v8 | Prose + --flags |
unbounded | --no X flag |
none; matte post | ≤15 chars with --text |
| Ideogram 3 / 3 Turbo | Prose + text in "double quotes" |
unbounded | native | dedicated POST /v1/ideogram-v3/generate-transparent endpoint; rendering_speed: "TURBO" for Turbo tier (no style:"transparent" param exists) |
~3–6 words reliable, ~10 with seed retries |
| Recraft V3 / V4 | Prose + controls.colors + style_id (V3 only — V4 dropped it) |
unbounded | native | native SVG handles alpha trivially | ~3–5 words |
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 · 179 lines · 95 tokens per session scan A cdbb38e91a0f
t2i-prompt-dialect is a skill published in the GitHub repository MohamedAbdallah-14/prompt-to-asset (19 stars, last pushed yesterday), licensed MIT. It adds 95 tokens to every session and 3,010 once invoked, about $0.0005 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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