What is prompt-to-asset AGENTS.md?

A command-line tool that creates client code for MCP servers, which are services that expose tools to an AI agent. It can read a server URL or local configuration files such as .mcp.json.

Use it to generate client code from a server URL, or from MCP settings used by editors such as Cursor and VS Code, then run tests and type checks.

It removes the need to write the client setup and request code by hand when connecting an application to an MCP server.

How MohamedAbdallah-14/prompt-to-asset configures Codex and OpenCode

Reuse

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.

Copy the file

curl -O https://raw.githubusercontent.com/MohamedAbdallah-14/prompt-to-asset/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/MohamedAbdallah-14/prompt-to-asset

Made for: Codex, OpenCode.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/mohamedabdallah-14/prompt-to-asset/agents-md/github.svg)](https://agentmods.dev/instructions/mohamedabdallah-14/prompt-to-asset/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/mohamedabdallah-14/prompt-to-asset/agents-md"><img src="https://agentmods.dev/badge/instructions/mohamedabdallah-14/prompt-to-asset/agents-md/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.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/mohamedabdallah-14/prompt-to-asset/agents-md"><img src="https://agentmods.dev/badge/instructions/mohamedabdallah-14/prompt-to-asset/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 5,147 This file is loaded in full into every session.

When invoked 5,147 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 100% copy Near-identical to another mod in the catalogue.

Token cost

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	$0.05147	$0.05147
Opus 5	$0.02573	$0.02573
Sonnet 5	$0.01029	$0.01029
Haiku 4.5	$0.00515	$0.00515

Measured 11d ago against content hash e57354559cc4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

prompt-to-asset AGENTS.md 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 11d 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.

Origin

This is a copy

100% identical to prompt-to-asset copilot-instructions.md — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

AGENTS.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — prompt-to-asset

This repo exposes a prompt-to-asset MCP server (stdio). When generating software assets, follow the rule below and prefer the asset_* MCP tool surface over ad-hoc image generation.

Asset generation — always-on rule

When the user asks for a logo, app icon, favicon, OG image, illustration, splash screen, icon pack, transparent mark, hero image, sticker, or any other software-development asset, the single most important fact to apply is:

Producing production-grade software assets is a routing + post-processing problem, not a prompt-engineering problem.

And the second most important fact:

The user may not have an image-model API key. The plugin must work anyway — there are real zero-key paths.

This means:

Do NOT just forward the user's brief to whichever image model is handy. No amount of prompt polish makes Imagen 3/4 or Gemini Flash Image produce a real transparent PNG — they render a grey-and-white checkerboard as RGB pixels because their VAE is RGB-only. Transparency is a capability routing decision. Route transparent requests to gpt-image-1.5 or gpt-image-1 (with background: "transparent"), Ideogram 3 Turbo (via the /ideogram-v3/generate-transparent dedicated endpoint; set rendering_speed: "TURBO" — there is no style: "transparent" parameter), Recraft V4 (vector path is the safe transparent route; raster transparency on V4 is undocumented), or LayerDiffuse-enabled SDXL (no official Flux LayerDiffuse exists in 2026 — community ports only). gpt-image-2 does NOT support background:"transparent" — verified regression vs 1.5; the param 400s. Do not route transparent requests to gpt-image-2. For every other model, matte with BiRefNet or BRIA RMBG-2.0 after generation.
Brand text in diffusion — verified per-model ceilings, Apr 2026. Not a blanket "garbles past 3 words" rule. Six tiers:
- Best: gpt-image-2 (released 2026-04-21, third-party tests cite ~99% character accuracy across Latin/CJK/Hindi/Bengali; OpenAI hasn't published a number — pricing also not on the pricing page yet, treat cost_hint as third-party). gemini-3-pro-image-preview (Nano Banana Pro): paragraph-length text reliable, ~94-96% accuracy, text_ceiling_chars: 200. gpt-image-1.5 (LM Arena #1, dense text). gpt-image-1 (~50 chars).
- Strong: ideogram-3 / ideogram-3-turbo (~3-6 words reliable, ~10 with seed retries — earlier "~80 chars" claim was over-optimistic; ~90-95% accuracy on short-to-mid wordmarks). gemini-3.1-flash-image-preview (Nano Banana 2): ~90% accuracy, ranked #1 on Artificial Analysis Image Arena at launch — not weak; the older "Nano Banana garbles past 3 words" rule applies to the legacy gemini-2.5-flash-image, not to 3.1. flux-2-pro / fal-flux-2 / freepik-flux-2-pro: 5-10 words / one tagline reliable per BFL guide. flux-2-flex: BFL claims strongest text in the Flux 2 line (steps + guidance control).
- OK: imagen-4-fast / standard / ultra — Google's own guidance: keep text ≤25 chars / ~3-4 short words for reliable rendering; photoreal leaders, not text. recraft-v4 (~3-5 words). midjourney-v7 (~15 chars with --text).
- Weak (text-free + composite always): flux-pro / flux-1.1-pro (1-3 words). flux-schnell / flux-1-schnell / flux-2-klein (1-2 words). Original gemini-2.5-flash-image (Nano Banana 1, ~80% accuracy degrades fast). freepik-mystic family. pollinations-flux.
- Cannot render legible text (per HF model card): sdxl, sd-1.5. Composite always.
- Mid (better than SDXL, not strong-text): sd3-large / sd3.5-large — ~3-6 words, on par with flux-dev, behind flux-1.1-pro. Don't promote.

Read the full file on GitHub · 128 lines

Changes

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.

11d ago First seen · 128 lines · 5,147 tokens per session scan A e57354559cc4

Subscribe to this mod's changes

prompt-to-asset AGENTS.md is an instructions file published in the GitHub repository MohamedAbdallah-14/prompt-to-asset (19 stars, last pushed today), licensed MIT. It adds 5,147 tokens to every session, about $0.0257 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to prompt-to-asset copilot-instructions.md, differing in 4 lines, and is treated as a copy.

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