ai-artist

name	ai-artist
version	1.0.0
description	[AI & Tools] Write and optimize prompts for AI-generated outcomes across text and image models. Use when crafting prompts for LLMs (Claude, GPT, Gemini), image generators (Midjourney, DALL-E, Stable Diffusion, Imagen, Flux), or video generators (Veo, Runway). Covers prompt structure, style keywords, negative prompts, chain-of-thought, few-shot examples, iterative refinement, and domain-specific patterns for marketing, code, and creative writing.
allowed-tools	NONE
license	MIT

[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI may ask user whether to skip.

Quick Summary

Goal: Write and optimize prompts for AI text, image, and video generation models (Claude, GPT, Midjourney, DALL-E, Stable Diffusion, Flux, Veo).

Workflow:

1. Identify — Determine model type (LLM, image, video) and desired outcome
2. Structure — Apply model-specific prompt patterns (Role/Context/Task for LLMs, Subject/Style/Composition for images)
3. Refine — Iterate with A/B testing, style keywords, negative prompts

Key Rules:

• Use clarity, context, structure, and iteration as core principles
• Apply model-specific syntax (Midjourney --ar, SD weighted tokens, etc.)
• Load reference files for detailed guidance per domain (marketing, code, writing, data)

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

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IMPORTANT Task Planning Notes (MUST FOLLOW)

• Always plan and break work into many small todo tasks
• Always add a final review todo task to verify work quality and identify fixes/enhancements