Find your landing page message
Connect a product’s value to a specific audience and write a clear first-screen message.
Craft prompts for AI models (text, image, video). Use for Midjourney, DALL-E, Stable Diffusion, Flux, Veo, prompt engineering, style keywords, negative prompts, iterative refinement.
---
name: ai-artist-435b08572850
description: Craft prompts for AI models (text, image, video). Use for Midjourney, DALL-E, Stable Diffusion, Flux, Veo, prompt engineering, style keywords, negative prompts, iterative refinement.
version: 1.0.0
license: MIT
---
# AI Artist - Prompt Engineering
Craft effective prompts for AI text and image generation models.
## Core Principles
1. **Clarity** - Be specific, avoid ambiguity
2. **Context** - Set scene, role, constraints upfront
3. **Structure** - Use consistent formatting (markdown, XML tags, delimiters)
4. **Iteration** - Refine based on outputs, A/B test variations
## Quick Patterns
### LLM Prompts (Claude/GPT/Gemini)
```
[Role] You are a {expert type} specializing in {domain}.
[Context] {Background information and constraints}
[Task] {Specific action to perform}
[Format] {Output structure - JSON, markdown, list, etc.}
[Examples] {1-3 few-shot examples if needed}
```
### Image Generation (Midjourney/DALL-E/Stable Diffusion)
```
[Subject] {main subject with details}
[Style] {artistic style, medium, artist reference}
[Composition] {framing, angle, lighting}
[Quality] {resolution modifiers, rendering quality}
[Negative] {what to avoid - only if supported}
```
**Example**: `Portrait of a cyberpunk hacker, neon lighting, cinematic composition, detailed face, 8k, artstation quality --ar 16:9 --style raw`
## References
Load for detailed guidance:
| Topic | File | Description |
|-------|------|-------------|
| LLM | `references/llm-prompting.md` | System prompts, few-shot, CoT, output formatting |
| Image | `references/image-prompting.md` | Style keywords, model syntax, negative prompts |
| Nano Banana | `references/nano-banana.md` | Gemini image prompting, narrative style, multi-image input |
| Advanced | `references/advanced-techniques.md` | Meta-prompting, chaining, A/B testing |
| Domain Index | `references/domain-patterns.md` | Universal pattern, links to domain files |
| Marketing | `references/domain-marketing.md` | Headlines, product copy, emails, ads |
| Code | `references/domain-code.md` | Functions, review, refactoring, debugging |
| Writing | `references/domain-writing.md` | Stories, characters, dialogue, editing |
| Data | `references/domain-data.md` | Extraction, analysis, comparison |
## Model-Specific Tips
| Model | Key Syntax |
|-------|------------|
| Midjourney | `--ar`, `--style`, `--chaos`, `--weird`, `--v 6.1` |
| DALL-E 3 | Natural language, no parameters, HD quality option |
| Stable Diffusion | Weighted tokens `(word:1.2)`, LoRA, negative prompt |
| Flux | Natural prompts, style mixing, `--guidance` |
| Imagen/Veo | Descriptive text, aspect ratio, style references |
## Anti-Patterns
- Vague instructions ("make it better")
- Conflicting constraints
- Missing context for domain tasks
- Over-prompting with redundant details
- Ignoring model-specific strengths/limits
---
name: ai-artist-435b08572850
description: Craft prompts for AI models (text, image, video). Use for Midjourney, DALL-E, Stable Diffusion, Flux, Veo, prompt engineering, style keywords, negative prompts, iterative refinement.
version: 1.0.0
license: MIT
---
# AI Artist - Prompt Engineering
Craft effective prompts for AI text and image generation models.
## Core Principles
1. **Clarity** - Be specific, avoid ambiguity
2. **Context** - Set scene, role, constraints upfront
3. **Structure** - Use consistent formatting (markdown, XML tags, delimiters)
4. **Iteration** - Refine based on outputs, A/B test variations
## Quick Patterns
### LLM Prompts (Claude/GPT/Gemini)
```
[Role] You are a {expert type} specializing in {domain}.
[Context] {Background information and constraints}
[Task] {Specific action to perform}
[Format] {Output structure - JSON, markdown, list, etc.}
[Examples] {1-3 few-shot examples if needed}
```
### Image Generation (Midjourney/DALL-E/Stable Diffusion)
```
[Subject] {main subject with details}
[Style] {artistic style, medium, artist reference}
[Composition] {framing, angle, lighting}
[Quality] {resolution modifiers, rendering quality}
[Negative] {what to avoid - only if supported}
```
**Example**: `Portrait of a cyberpunk hacker, neon lighting, cinematic composition, detailed face, 8k, artstation quality --ar 16:9 --style raw`
## References
Load for detailed guidance:
| Topic | File | Description |
|-------|------|-------------|
| LLM | `references/llm-prompting.md` | System prompts, few-shot, CoT, output formatting |
| Image | `references/image-prompting.md` | Style keywords, model syntax, negative prompts |
| Nano Banana | `references/nano-banana.md` | Gemini image prompting, narrative style, multi-image input |
| Advanced | `references/advanced-techniques.md` | Meta-prompting, chaining, A/B testing |
| Domain Index | `references/domain-patterns.md` | Universal pattern, links to domain files |
| Marketing | `references/domain-marketing.md` | Headlines, product copy, emails, ads |
| Code | `references/domain-code.md` | Functions, review, refactoring, debugging |
| Writing | `references/domain-writing.md` | Stories, characters, dialogue, editing |
| Data | `references/domain-data.md` | Extraction, analysis, comparison |
## Model-Specific Tips
| Model | Key Syntax |
|-------|------------|
| Midjourney | `--ar`, `--style`, `--chaos`, `--weird`, `--v 6.1` |
| DALL-E 3 | Natural language, no parameters, HD quality option |
| Stable Diffusion | Weighted tokens `(word:1.2)`, LoRA, negative prompt |
| Flux | Natural prompts, style mixing, `--guidance` |
| Imagen/Veo | Descriptive text, aspect ratio, style references |
## Anti-Patterns
- Vague instructions ("make it better")
- Conflicting constraints
- Missing context for domain tasks
- Over-prompting with redundant details
- Ignoring model-specific strengths/limits
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.