[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 MUST 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:

• Identify — Determine model type (LLM, image, video) and desired outcome

• Structure — Apply model-specific prompt patterns (Role/Context/Task for LLMs, Subject/Style/Composition for images)

• 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)

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

AI Artist - Prompt Engineering

Craft effective prompts for AI text and image generation models.

Core Principles

• Clarity - Be specific, avoid ambiguity

• Context - Set scene, role, constraints upfront

• Structure - Use consistent formatting (markdown, XML tags, delimiters)

• 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

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