image-generation

image-generation is a skill for Claude Code, Codex from OpsinTech/opsintech-platform. It costs 42 tokens per session (1,903 once invoked), scanned A, a copy of image-generation, MIT.

An image-creation workflow for making visuals such as characters, scenes, and products. It uses structured prompts, optional reference images, and an automated script.

In plain words
What is it for?
Use it to plan and generate images, guide style or composition with reference images, and handle different visual-creation requests.
Why use it?
It gives image requests a consistent format and a defined process, including style, composition, lighting, and output details.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to plan and generate images, guide style or composition with reference images, and handle different visual-creation requests.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/opsintech/opsintech-platform/image-generation
Install

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.

Any agent
npx skills add OpsinTech/opsintech-platform --skill image-generation
Clone the repo
git clone --depth 1 https://github.com/OpsinTech/opsintech-platform

Made for: Claude Code, Codex.

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.

agentmods badge for image-generation

README.md
[![agentmods](https://agentmods.dev/badge/skills/opsintech/opsintech-platform/image-generation/github.svg)](https://agentmods.dev/skills/opsintech/opsintech-platform/image-generation)
Your own site
<a href="https://agentmods.dev/skills/opsintech/opsintech-platform/image-generation"><img src="https://agentmods.dev/badge/skills/opsintech/opsintech-platform/image-generation/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.

agentmods 80×15 button for image-generation

Your own site · 80×15
<a href="https://agentmods.dev/skills/opsintech/opsintech-platform/image-generation"><img src="https://agentmods.dev/badge/skills/opsintech/opsintech-platform/image-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,903 The whole file, excluding the scripts and references it only reads on demand.
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.01903
Opus 5.5 $0.00017 $0.00761
Sonnet 5.5 $0.00008 $0.00381
Haiku 4.5 $0.00004 $0.00190

Measured 1mo ago against content hash 4928a28377df, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

image-generation 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 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/generate.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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 image-generation — 0 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.

skills/public/image-generation/SKILL.md · 188 lines

How it starts

The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Image Generation Skill

Overview

This skill generates high-quality images using structured prompts and a Python script. The workflow includes creating JSON-formatted prompts and executing image generation with optional reference images.

Core Capabilities

  • Create structured JSON prompts for AIGC image generation
  • Support multiple reference images for style/composition guidance
  • Generate images through automated Python script execution
  • Handle various image generation scenarios (character design, scenes, products, etc.)

Workflow

Step 1: Understand Requirements

When a user requests image generation, identify:

  • Subject/content: What should be in the image
  • Style preferences: Art style, mood, color palette
  • Technical specs: Aspect ratio, composition, lighting
  • Reference images: Any images to guide generation
  • You don't need to check the folder under /mnt/user-data

Step 2: Create Structured Prompt

Generate a structured JSON file in /mnt/user-data/workspace/ with naming pattern: {descriptive-name}.json

Step 3: Execute Generation

Call the Python script:

python /mnt/skills/public/image-generation/scripts/generate.py \
  --prompt-file /mnt/user-data/workspace/prompt-file.json \
  --reference-images /path/to/ref1.jpg /path/to/ref2.png \
  --output-file /mnt/user-data/outputs/generated-image.jpg
  --aspect-ratio 16:9

Parameters:

  • --prompt-file: Absolute path to JSON prompt file (required)
  • --reference-images: Absolute paths to reference images (optional, space-separated)
  • --output-file: Absolute path to output image file (required)
  • --aspect-ratio: Aspect ratio of the generated image (optional, default: 16:9)

[!NOTE] Do NOT read the python file, just call it with the parameters.

Character Generation Example

User request: "Create a Tokyo street style woman character in 1990s"

Create prompt file: /mnt/user-data/workspace/asian-woman.json

{
  "characters": [{
    "gender": "female",
    "age": "mid-20s",
    "ethnicity": "Japanese",
    "body_type": "slender, elegant",
    "facial_features": "delicate features, expressive eyes, subtle makeup with emphasis on lips, long dark hair partially wet from rain",
    "clothing": "stylish trench coat, designer handbag, high heels, contemporary Tokyo street fashion",
    "accessories": "minimal jewelry, statement earrings, leather handbag",
    "era": "1990s"
  }],
  "negative_prompt": "blurry face, deformed, low quality, overly sharp digital look, oversaturated colors, artificial lighting, studio setting, posed, selfie angle",
  "style": "Leica M11 street photography aesthetic, film-like rendering, natural color palette with slight warmth, bokeh background blur, analog photography feel",
  "composition": "medium shot, rule of thirds, subject slightly off-center, environmental context of Tokyo street visible, shallow depth of field isolating subject",
  "lighting": "neon lights from signs and storefronts, wet pavement reflections, soft ambient city glow, natural street lighting, rim lighting from background neons",
  "color_palette": "muted naturalistic tones, warm skin tones, cool blue and magenta neon accents, desaturated compared to digital photography, film grain texture"
}

Read the full file on GitHub · 188 lines

Files

What ships with it

2 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.

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.

  1. 1mo ago First seen · 188 lines · 42 tokens per session scan A 4928a28377df

Subscribe to this mod's changes

image-generation is a skill published in the GitHub repository OpsinTech/opsintech-platform (94 stars, last pushed 3mo ago), licensed MIT. It adds 42 tokens to every session and 1,903 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to image-generation, differing in 0 lines, and is treated as a copy.

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