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 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 .