Awesome-omni-skill midjourney-prompt-engineering

Use when generating images with Midjourney, constructing MJ prompts, iterating on MJ output quality, choosing between --sref/--oref/style codes, scoring image results, or building reusable prompt patterns. Also use when exploring MJ style codes, animating images, or debugging why a prompt isn't producing the intended result.

install

source · Clone the upstream repo

git clone https://github.com/diegosouzapw/awesome-omni-skill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/development/midjourney-prompt-engineering" ~/.claude/skills/diegosouzapw-awesome-omni-skill-midjourney-prompt-engineering && rm -rf "$T"

manifest: skills/development/midjourney-prompt-engineering/SKILL.md

source content

Midjourney Prompt Learning System

A skill that knows Midjourney. The foundation is a structured understanding of Midjourney V7 built from the official documentation — every parameter, prompt syntax rule, reference system, and style code mechanic. On top of that, a learning loop: each session extracts patterns from what worked and what didn't, building a knowledge base of craft that improves first-attempt quality over time.

Architecture

You are a multimodal reasoning model. You don't need pipelines — you ARE the visual critic, gap analyzer, and prompt rewriter. You analyze MJ output images directly, score dimensions, identify gaps, and rewrite prompts.

The one thing you can't do natively is remember across sessions. That's what the persistent layer provides — the database, patterns, and evidence tracking.

Knowledge Foundation (ships with the skill)

File	What It Contains	Source
`knowledge/v7-parameters.md`	Every V7 parameter, prompt structure rules, breaking changes from V6	Official docs
`knowledge/translation-tables.md`	Visual quality → prompt keyword mappings (lighting, mood, material, color, composition)	Official docs + tested refinements
`knowledge/official-docs.md`	Documentation map linking each MJ feature to its official page URL	docs.midjourney.com
`knowledge/failure-modes.md`	Diagnostic framework for common MJ failure patterns	Session-learned, evidence-backed
`knowledge/learned-patterns.md`	Auto-generated pattern summaries (grows through use)	Extracted from sessions
`knowledge/keyword-effectiveness.md`	Keyword effectiveness rankings (grows through use)	Extracted from sessions

The static files (

v7-parameters

translation-tables

official-docs

) are the skill's baseline knowledge — what a skilled MJ user would know from reading the documentation carefully. The dynamic files (

failure-modes

learned-patterns

keyword-effectiveness

) are populated through real sessions and grow over time.

Module Dependencies

Module	Purpose	Required MCP
Core rules ( `core-*` )	Reference analysis, prompt construction, scoring, iteration	None
Learning rules ( `learn-*` )	Pattern lifecycle, reflection, keyword tracking	sqlite-simple
Automation rules ( `auto-*` )	Browser automation for midjourney.com	playwright

Core only (manual): Load

core-*

rules. Copy prompts to MJ manually. Core + Learning: Add

learn-*

rules + sqlite MCP. Patterns persist across sessions. Full system: Add

auto-*

rules + playwright MCP. Hands-free iteration.

# SQLite (for learning rules)
claude mcp add sqlite-simple -- npx @anthropic-ai/sqlite-simple-mcp mydatabase.db

# Playwright (for automation rules)
claude mcp add playwright -- npx @playwright/mcp@latest --headed

# Initialize the database
sqlite3 mydatabase.db < schema.sql

Rules Quick Reference

Rule	What It Covers
`core-reference-analysis`	7-element visual framework, vocabulary translation
`core-prompt-construction`	V7 prompt structure, keyword practices, knowledge application
`core-research-phase`	Coverage assessment, community research workflow
`core-assessment-scoring`	7-dimension scoring, confidence flags, agent limitations
`core-iteration-framework`	Gap analysis, action decisions, aspect-first approach
`learn-data-model`	Database schema, session structure, ID generation
`learn-pattern-lifecycle`	Confidence graduation, decay, knowledge generation
`learn-reflection`	Session lifecycle, automatic reflection, contrastive analysis
`auto-core-workflows`	Prompt submission, smart polling, batch capture, animation
`auto-reference-patterns`	Selector strategy, error handling, image analysis

Scoring

All iterations scored on 7 dimensions: subject, lighting, color, mood, composition, material, spatial. All 7 always scored (1.0 for "not applicable"). Scores are preliminary until user-validated. See

rules/core-assessment-scoring.md

Commands

Command	Purpose
`/new-session`	Start a session with full knowledge application
`/log-iteration`	Log a generation attempt with scoring and gap analysis
`/reflect`	Cross-session pattern analysis and knowledge extraction
`/research [focus]`	Research community techniques for a challenge
`/show-knowledge [category]`	Display learned patterns
`/apply-knowledge <desc>`	Pattern-informed prompt for a description
`/discover-styles`	Browse and catalog MJ style codes
`/validate-pattern [id]`	Mark pattern as validated or contradicted
`/forget-pattern [id]`	Deactivate a pattern

Key Principle

Every pattern must have logged evidence. The system learns from real iteration data, not assumptions. Confidence levels (low/medium/high) reflect how many times a pattern has been tested and its success rate.

Full Reference

For the complete compiled reference combining all rules, see

AGENTS.md