Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add leggiemint/legnext-skills --skill midjourney-promptinggit clone --depth 1 https://github.com/leggiemint/legnext-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/leggiemint/legnext-skills/midjourney-prompting)<a href="https://agentmods.dev/skills/leggiemint/legnext-skills/midjourney-prompting"><img src="https://agentmods.dev/badge/skills/leggiemint/legnext-skills/midjourney-prompting/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/leggiemint/legnext-skills/midjourney-prompting"><img src="https://agentmods.dev/badge/skills/leggiemint/legnext-skills/midjourney-prompting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.03117 |
| Opus 5 | $0.00034 | $0.01558 |
| Sonnet 5 | $0.00013 | $0.00623 |
| Haiku 4.5 | $0.00007 | $0.00312 |
Grade A, and why
midjourney-prompting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Midjourney Prompting
Expert guidance for crafting effective Midjourney prompts using professional techniques and frameworks.
When to Use This Skill
Use this skill when users need:
- Converting natural language descriptions into effective Midjourney prompts
- Optimizing existing prompts for better results
- Guidance on Midjourney parameters (--ar, --s, --v, etc.)
- Understanding prompt structure and best practices
- Photography terminology and techniques for photorealistic images
- Style references and advanced prompting techniques
The 7-Element Framework
The most systematic approach to crafting Midjourney prompts. Use this framework to ensure comprehensive, effective prompts:
| Element | Purpose | Examples |
|---|---|---|
| Subject | What's in the image | portrait, landscape, product, character, scene |
| Medium | Visual style/art form | photography, oil painting, digital art, watercolor |
| Environment | Setting/location | studio, forest, city street, underwater, space |
| Lighting | Light quality/direction | golden hour, studio lighting, dramatic shadows, soft diffused |
| Color | Color palette/mood | vibrant, muted, monochrome, warm tones, cool blues |
| Mood | Emotional tone | serene, energetic, mysterious, joyful, melancholic |
| Composition | Framing/perspective | close-up, wide angle, bird's eye view, rule of thirds |
Quick structure:
[Subject] [Description], [Medium], [Environment], [Lighting], [Color palette], [Mood], [Composition] [Parameters]
Prompt Engineering Process
Step 1: Understand the User's Intent
Ask clarifying questions if needed:
- What's the main subject?
- What style are they aiming for? (photorealistic, illustrated, artistic)
- What mood or atmosphere do they want?
- What's the intended use? (social media, print, concept art)
- Any technical requirements? (aspect ratio, quality level)
Step 2: Transform Natural Language to Structured Prompt
Example transformations:
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 328 lines · 67 tokens per session scan A 8b259c1a11c3
midjourney-prompting is a skill published in the GitHub repository leggiemint/legnext-skills (4 stars, last pushed 8mo ago), licensed MIT. It adds 67 tokens to every session and 3,117 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
kanso-prompting
Use when rewriting, sharpening, or producing a prompt for Claude or another current frontier model. Sets standing rules for getting better LLM output through better prompts. Loaded by /kanso-task; not directly invoked by the user.
ronald
Invoke when comparing models or prompts and you want evidence, not a vibe check — choosing a model for a task (opus vs sonnet vs haiku; Claude vs Codex vs Gemini), A/B testing prompt variants, running a model/prompt bake-off or eval, or confirming a prompt change actually helped rather than got lucky. Runs a…
context-engineering
Guidance for designing, auditing, and improving context supplied to LLM agents, prompts, tools, RAG/MCP servers, and multi-agent systems. Use when the task involves context selection, prompt or agent architecture, tool descriptions, retrieval context, memory, compaction, autonomy, or evaluation of LLM behavior.
skill-meta-prompt
Craft better prompts using proven optimization techniques — use when your prompt needs refinement.
add-ai-webapi
Integrates Power Pages generative-AI summarization APIs (PREVIEW) into a Single Page Application (SPA) site — the Search Summary API and the Data Summarization API — on any record-detail or list page. Generates per-target service code (CSRF-handled) and AI site settings; delegates Web API settings, table permissions…
add-workiq
Adds Work IQ (M365 Copilot Search) to a Power Apps code app via the Work IQ Copilot MCP connector (shareda365copilotchatmcp), then wires up a production-ready McpSession wrapper for AI-powered, knowledge-grounded search and chat. Use when integrating Microsoft 365 Copilot search/chat. The CopilotChat tool searches…