smithery/binhmuc

ai-artist

Write and optimize prompts for AI-generated outcomes across text and image models.

Installation

$ npx skills add smithery/binhmuc --skill ai-artist

Summary

  • 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.

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from smithery/binhmuc.

npx skills add smithery/binhmuc

Browse all from smithery/binhmuc

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0
LicenseMIT

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,137 B
  • docs SUMMARY.md 453 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

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