Borrowing it
Nothing to install: this file belongs to recomposesh/recompose. 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/recomposesh/recompose/main/.agents/skills/image-generation/SKILL.mdgit clone --depth 1 https://github.com/recomposesh/recomposeWrote 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/recomposesh/recompose/image-generation)<a href="https://agentmods.dev/skills/recomposesh/recompose/image-generation"><img src="https://agentmods.dev/badge/skills/recomposesh/recompose/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.
<a href="https://agentmods.dev/skills/recomposesh/recompose/image-generation"><img src="https://agentmods.dev/badge/skills/recomposesh/recompose/image-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00032 | $0.02101 |
| Opus 5.5 | $0.00013 | $0.00840 |
| Sonnet 5.5 | $0.00006 | $0.00420 |
| Haiku 4.5 | $0.00003 | $0.00210 |
Grade C, and why
image-generation scanned grade C 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 1mo 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.
Instruction-override phrasinghighPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
- Bypass content policies of AI tools 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.
How it starts
The opening of the file, as written. The whole thing — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation Skill
Overview
I help you create effective prompts for AI image generation tools like DALL-E, Midjourney, and Stable Diffusion. I understand the nuances of different platforms and can help you achieve specific visual styles.
What I can do:
- Write detailed image generation prompts
- Optimize prompts for specific AI tools
- Suggest style keywords and modifiers
- Create negative prompts to avoid unwanted elements
- Adapt prompts for different aspect ratios
- Generate variations and alternatives
What I cannot do:
- Generate images directly
- Guarantee exact output from AI tools
- Predict how AI will interpret prompts
- Bypass content policies of AI tools
How to Use Me
Step 1: Describe Your Vision
Tell me:
- What you want to see in the image
- The purpose (presentation, social media, marketing)
- Style preferences (realistic, artistic, minimalist)
- Mood or emotion to convey
Step 2: Choose the Platform
- DALL-E 3: Best for clarity and instruction-following
- Midjourney: Best for artistic and aesthetic images
- Stable Diffusion: Most customizable, local options
Step 3: Specify Parameters
- Aspect ratio (1:1, 16:9, 4:3, etc.)
- Quality level
- Style references
- Things to avoid
Prompt Engineering Framework
Basic Prompt Structure
[Subject] + [Action/State] + [Environment] + [Style] + [Technical Parameters]
Detailed Template
[Main Subject]
- Who/what is the focus?
- What are they doing?
[Environment/Setting]
- Where is this taking place?
- Time of day? Weather? Season?
[Composition]
- Camera angle (eye-level, bird's eye, low angle)
- Framing (close-up, medium shot, wide shot)
- Focus (depth of field)
[Style]
- Art style (photorealistic, watercolor, oil painting, etc.)
- Artist reference (optional)
- Era/period
[Lighting]
- Type (natural, studio, dramatic, soft)
- Direction (backlit, side-lit, front-lit)
[Color]
- Palette (warm, cool, monochrome)
- Specific colors to include
[Mood/Atmosphere]
- Emotion to evoke
- Overall feeling
[Technical]
- Quality modifiers
- Aspect ratio
- Negative prompts
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.
- 1mo ago First seen · 375 lines · 32 tokens per session scan C 3a0a93b23716
image-generation is a skill published in the GitHub repository recomposesh/recompose (30 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 2,101 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it C with 1 finding (instruction-override phrasing). It is 100% identical to image-generation, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
image-generation
Create effective AI image generation prompts for DALL-E, Midjourney, and Stable Diffusion. Generate prompts for various styles and use cases.
vynly-post
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tensorboard
Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit.
experiment-tracking-swanlab
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infographic-creator
An information-graphic creation guide that turns written information into a visual layout using AntV Infographic, a description language for rendering infographics.
results-analysis
This skill should be used when the user asks to "analyze experimental results", "run strict statistical analysis", "compare model performance", "generate scientific figures", "check significance", "do ablation analysis", or mentions interpreting experiment data with rigorous statistics and visualization. It focuses on…