image-prompting

name	image-prompting
description	Use when generating or editing images via `blockrun_image` — especially with GPT Image 2, DALL-E 3, or Flux for posters, UI mockups, marketing assets, product shots, or anything with on-image text. Turns vague user requests ("make me a cool poster") into structured, text-accurate prompts that actually render what you asked for.
triggers	["image prompt","make a poster","create poster","ui mockup","marketing asset","product shot","gpt image 2","image with text","typography poster","social asset","generate marketing image","ai poster"]

Image Prompting

Most image failures are prompt failures. This skill gives the MCP agent a repeatable structure for turning any user request into a prompt that renders clean typography, preserves layout on edits, and avoids AI slop. Defaults are tuned for GPT Image 2 (best legible text), with fallbacks for DALL-E 3, Flux, and Nano Banana.

Quick Decision Table

User wants...	Model	Mode	Size	Cost
Poster / typography-heavy asset	openai/gpt-image-2	generate	1536x1024 or 1024x1536	~$0.04
Clean product / UI mockup	openai/gpt-image-2	generate	1024x1024	~$0.04
Photoreal / fashion / editorial	openai/gpt-image-2 or black-forest/flux-1.1-pro	generate	1024x1024+	$0.04–0.06
Artistic / stylized / fast	google/nano-banana	generate	1024x1024	~$0.01
Edit an existing image (localized change)	openai/gpt-image-2	edit	match source	~$0.04
Composite from multiple refs	openai/gpt-image-2	edit (multi-ref)	match target	~$0.04

Valid GPT Image 2 sizes: 1024x1024 (square), 1536x1024 (landscape ~3:2), 1024x1536 (portrait ~2:3).

The 5-Section Prompt Framework

Write prompts as five short blocks separated by blank lines. This is the single biggest quality lever.

SCENE: where/when/background/environment, one or two lines.

SUBJECT: the main focus (who/what), described concretely.

DETAILS: materials, texture, lighting, camera angle, composition, mood,
lens feel, depth of field, surface condition. Stack concrete nouns.

USE CASE: editorial photo / product mockup / poster / UI screen / infographic / concept frame.
(This single line tells the model what kind of image to produce.)

CONSTRAINTS: what must not drift. "No extra text." "No duplicate elements."
"Preserve face." "Legible typography." Repeat these on every edit.

The fifth slot is where most mediocre prompts fail silently. Describe the idea without bounding it and the model gets inventive in directions you will regret.

Text & Typography Rules (the #1 differentiator for GPT Image 2)

1. Wrap literal text in quotes or ALL CAPS. Headline (EXACT TEXT): "Fresh and clean."
2. Specify font style, weight, size, color, placement, letter-spacing.
3. Treat text as layout, not decoration: hero vs. sub vs. caption with hierarchy + spacing.
4. State: No extra words. No duplicate text. No watermarks.
5. Spell difficult words letter-by-letter if the model keeps breaking them.
6. Mark each distinct piece of copy with its role: HERO:, SUB:, BOTTOM-LEFT TAG:, TOP BANNER:.

Anti-Slop Rules (visual facts > excitement)

Bad (vague / praise-loaded)	Good (concrete visual fact)
"stunning, epic, masterpiece"	"overcast daylight, brushed aluminum, 50mm feel"
"minimalist brutalist luxury editorial"	"cream background, heavy black condensed sans-serif, asymmetric type block, one hero object, studio tabletop light"
"it should contain a boarding pass feel"	"a boarding pass lies on the tray, barcode visible, creased corner"
"beautiful lighting"	"incandescent work lamp spilling warm light onto wet concrete"

Rules:

• Visual facts over praise. Replace adjectives like gorgeous/stunning/incredible with observable specifics.
• Style tags need targets. Don't just name a style — describe the artifacts that style produces.
• Say the real thing. If the image must contain a boarding pass, say "boarding pass."
• Name the lens. 35mm, 50mm, medium format. Depth of field: shallow vs. deep.
• Name the light. Source + quality + direction + color temperature.

Instructions

1. Initialize

import os
from pathlib import Path

chain_file = Path.home() / ".blockrun" / ".chain"
chain = chain_file.read_text().strip() if chain_file.exists() else "base"

if chain == "solana":
    from blockrun_llm import setup_agent_solana_wallet, ImageClient
    setup_agent_solana_wallet()
else:
    from blockrun_llm import setup_agent_wallet, ImageClient
    setup_agent_wallet()

image = ImageClient()

2. Generate from Scratch

prompt = """
SCENE: A realistic roadside billboard at sunset, empty two-lane highway,
soft gradient sky from peach to lavender, a few utility poles.

SUBJECT: A product billboard for a bottled water brand. Bottle on the right
third of the frame, catching warm rim light.

DETAILS: 35mm photo feel, shallow depth of field, matte-painted billboard,
clean kerning, precise print finish.

USE CASE: Product mockup for a marketing deck, landscape 3:2.

CONSTRAINTS:
- Headline (EXACT TEXT): "Fresh and clean."
- Bold sans-serif, high contrast, centered vertically in the left half.
- No extra words. No duplicate text. No watermark.
"""

result = image.generate(
    prompt,
    model="openai/gpt-image-2",
    size="1536x1024",
    n=1,
)
print(result.data[0].url)  # URL or data URL

3. Edit: Change / Preserve / Constraints

The golden pattern for iterative editing — one small change per turn. Repeat the preserve list every turn.

prompt = """
CHANGE: Make the light warmer — shift the sunset toward a deeper orange.
         Remove the extra chair on the left.

PRESERVE: Keep the bottle position, label text, and billboard layout exactly
          as in the source image. Keep the headline text verbatim.

CONSTRAINTS: No extra text. No duplicate elements. Same aspect ratio.
"""

# `image` arg accepts a public URL OR a data URL (data:image/png;base64,...)
result = image.edit(
    prompt,
    image="https://example.com/source.png",
    model="openai/gpt-image-2",
    size="1536x1024",
)
print(result.data[0].url)

Why this works: small atomic edits compound reliably. Giant rewrites ("redo this but nicer") drift everything.

4. Multi-Reference Composition

Pass multiple reference images (up to ~16) via the edit endpoint. Label each reference's role in the prompt so the model knows how to use it.

prompt = """
COMPOSITE: Combine the three reference images as follows.
- REF 1 is the SUBJECT (the wristwatch): preserve exact dial, hands, and crown.
- REF 2 is the ENVIRONMENT (marble tabletop + window light): use as background.
- REF 3 is the STYLE REFERENCE: match its color grade and contrast.

USE CASE: E-commerce hero shot, square.

CONSTRAINTS: No extra objects. No text. Preserve watch proportions exactly.
"""

# SDK: pass primary via `image=`; additional refs via a multipart request
# (check the MCP's `blockrun_image` tool for the multi-image payload shape)

Worked Example: "make me a cool poster announcing 100 trillion tokens on blockrun.ai"

This is the real prompt that produced the image below — a vague, one-line user ask turned into a structured prompt that rendered every copy line correctly on the first shot.

User asked for: "generate 1 cool poster showing we hit 100 Trillion Token LLM consumption on blockrun.ai"

Clarifying questions worth asking before prompting:

• Audience → social media flex for X/Twitter
• Aesthetic → retro-futuristic synthwave
• Hero text → "100 TRILLION TOKENS"
• Supporting copy → "The world's largest pay-per-call LLM gateway" · "Served on blockrun.ai" · "Powered by x402 micropayments" · "Now with Seedance + GPT Image 2"
• Aspect ratio → 16:9 landscape for X timeline

Final prompt passed to openai/gpt-image-2 at size="1536x1024":

SCENE: A retro-futuristic synthwave scene, 80s vaporwave aesthetic, cinematic
16:9 composition. Deep purple-to-magenta sunset sky with a giant glowing
pink-and-orange setting sun cut by thin horizontal neon lines. Palm tree
silhouettes on both sides. Faint city skyline in the distance. An infinite
chrome grid floor vanishing at the horizon with pink and cyan perspective lines.

SUBJECT: A milestone announcement poster with the hero text
"100 TRILLION TOKENS" dominating the center of the frame.

DETAILS: Hero text in huge glossy chrome letters with a pink-to-cyan gradient
and neon rim light, bold condensed sans-serif, CRT glow, slight scanline
texture across the letters. Faint CRT scanlines overlay the entire frame.
Subtle film grain. Chromatic aberration on edges. High contrast, symmetrical,
cinematic poster composition.

USE CASE: Social media announcement poster for X/Twitter, 16:9 landscape.

CONSTRAINTS:
- HERO (EXACT TEXT, centered): "100 TRILLION TOKENS"
- SUB under the hero (clean neon cyan, wide letter-spacing, EXACT TEXT):
  "The world's largest pay-per-call LLM gateway"
- TOP-CENTER BANNER inside a thin neon outline (EXACT TEXT):
  "NOW WITH SEEDANCE + GPT IMAGE 2"
- BOTTOM-LEFT TAG (monospace, magenta, EXACT TEXT):
  "> Served on blockrun.ai"
- BOTTOM-RIGHT TAG (monospace, magenta, EXACT TEXT):
  "> Powered by x402 micropayments"
- Legible crisp typography. No extra words. No duplicate text. No watermark.

Why this worked:

1. Every piece of copy got a role label (HERO / SUB / BANNER / TAG) with position and style — no guessing.
2. Every text string was marked (EXACT TEXT) and wrapped in quotes.
3. Concrete visual facts (CRT scanlines, chrome gradient, palm silhouettes) replaced vague words like "cool" and "awesome."
4. No duplicate text. No extra words. was in the CONSTRAINTS block — GPT Image 2 loves to duplicate headlines if you don't forbid it.
5. Aspect ratio chosen from the valid GPT Image 2 set (1536x1024) rather than asking for "16:9" and hoping.

Common Workflows

Poster / social asset

Use 1536x1024 for X/Twitter, 1024x1024 for IG grid, 1024x1536 for IG story. Put every copy element on its own labeled line in the CONSTRAINTS block. Always include No duplicate text. No extra words.

UI screen mockup

State the device, status bar, app name, screen title, each visible element with position and state (checked, active, disabled), palette (hex-ish is fine: "deep navy accent"), typography scale, corner radius, spacing feel.

Product shot

Surface + light source + lens + depth of field + one hero object. Name the material of everything in frame. Avoid "beautiful" — describe what you'd see.

Concept/brand exploration

Generate 3–4 variants with the same 5-section skeleton but swap only the DETAILS block (palette, lens, material). Keep SCENE/SUBJECT/USE CASE/CONSTRAINTS identical so you're actually comparing one variable.

Prompt Template (copy-paste and fill in)

SCENE: <where/when/background>

SUBJECT: <main focus, concrete nouns>

DETAILS: <materials, texture, lighting (source + quality + color),
         camera angle, lens feel, depth of field, composition, mood>

USE CASE: <poster | UI screen | product shot | editorial photo | concept frame>

CONSTRAINTS:
- HERO (EXACT TEXT): "<verbatim copy>"
- SUB: "<verbatim copy>"
- <other copy with role labels>
- <typography rules: font style, weight, spacing, hierarchy>
- No extra words. No duplicate text. No watermark.
- <anything that must not drift: face, layout, aspect ratio>

Three Operating Modes (at a glance)

Mode	When	Endpoint	Pattern
Generate	From scratch	/v1/images/generations	5-section framework
Edit	One image, localized change	/v1/images/image2image	Change / Preserve / Constraints
Combine	Multi-image composition	/v1/images/image2image (multi-ref)	Labeled refs (SUBJECT / ENV / STYLE)

Notes & Gotchas

• GPT Image 2 is currently the best for legible on-image text. For artistic prompts with no copy, Nano Banana is 4× cheaper and often prettier.
• Response shape: result.data[0].url is either an HTTPS URL or a data:image/...;base64,... string. Save via urllib.request.urlretrieve for URLs or base64.b64decode(item.b64_json) for b64 payloads.
• Aspect ratio: only the three sizes above are valid for GPT Image 2. If the user asks for 16:9, use 1536x1024 — it reads as landscape on X/Twitter without cropping.
• Iterative edits drift. Repeat the PRESERVE list every turn, even when it feels redundant.
• If text keeps breaking: shorten it, spell difficult words, and move it to a dedicated CONSTRAINTS line marked (EXACT TEXT).
• Never use words like amazing, stunning, masterpiece, ultra-detailed, 8k, trending on artstation — they waste tokens and pull toward generic AI-slop aesthetics.

Requirements

• BlockRun SDK: pip install blockrun-llm
• USDC wallet funded (ImageClient().get_wallet_address(); setup_agent_wallet().get_balance())
• For edit / multi-ref: source images must be reachable by a public URL or passed as a data URL

Reference

BlockRun image models: openai/gpt-image-2, openai/gpt-image-1, openai/dall-e-3, google/nano-banana, google/nano-banana-pro, black-forest/flux-1.1-pro, zai/cogview-4, xai/grok-imagine-image, xai/grok-imagine-image-pro