How to Use Goofy AI Images: A Practical Walkthrough

Why AI Image Generation Feels Harder Than It Should

Generating AI images should be easy by now. Instead, most people get stuck before they've created a single picture worth keeping.

Here's the honest reality: the market has matured. As Zapier's testing team puts it, "the top 50 or so models are all excellent, so other features and usability matter more than they used to." Quality is no longer the differentiator — workflow is. The hard part isn't making a good image; it's everything around making it.

The pain points start with choosing a tool. Pricing varies wildly: Nano Banana 2 is free with upgrades from $20/month, Adobe Firefly starts at $10/month, and Midjourney's Pro plan runs $60/month — and companies earning over $1M/year must be on Pro or Mega for commercial use, per Midjourney's licensing terms. Picking wrong can mean paying twice.

Then comes iteration. Generation speeds range from roughly 4–30 seconds on Midjourney's fast modes to nearly 2 minutes on heavier models — and results stay inconsistent even within a single tool. One independent review of Midjourney v8 found it "roughly on par with V7, and in some cases, actually worse," despite vendor claims of better prompt adherence. Meanwhile, legal risk is rising: Disney, Universal and Warner Bros. are suing Midjourney over copyright infringement, and Adobe now markets Firefly as "commercially safe" — trained only on licensed and public-domain content.

The practical consensus emerging among reviewers: no single tool does it all. That same review concluded Midjourney "can no longer be your only (or even primary) AI image generator," recommending it for style and moodboard brainstorming while pairing it with Nano Banana 2 for structured generation. Experienced creators increasingly treat each model as a specialist with its own "personality."

So where does that leave you? Chasing four separate problems at once:

• Choosing a generator from dozens of options with pricing from free to $60/month
• Writing prompts detailed enough to produce consistent, usable results
• Editing and iterating without degrading the image into something "wonky and ultimately unusable"
• Crediting and disclosing AI content to stay on the right side of emerging norms and lawsuits

The fix is to stop treating image generation as one big mystery and break it into a repeatable four-step workflow: pick a service, write a detailed prompt, edit, then credit and disclose. And if you'd rather automate parts of that loop, agent skills — like the documented image-generation skill pack for Claude Code — can handle prompt building and optimization for you. SkillzBench is building a free directory and MCP connector to help you find and test skills like these before deploying them.

The Four-Step Workflow That Works With Any Generator

Every AI image generator on the market works essentially the same way, which means one workflow can carry you through all of them. The four steps below come from hands-on testing and practitioner documentation, and they hold up whether you're using a free tool or a paid subscription.

Step 1: Pick a service that fits the task and your budget. CNET's hands-on testing rates Google's Nano Banana 2 highest at 8.5 out of 10, followed by Adobe Firefly at 8.0 and Canva at 7.5. Speed varies too — most generators finish in under two minutes, with faster services producing images in 10 to 30 seconds, so match the tool to how often you'll actually use it.

Step 2: Write a detailed prompt, not a keyword list. A widely used practitioner framework breaks every prompt into five parts: image type, subject, environment, technical specs, and constraints. Narrative prompts consistently outperform "keyword soup," and the guidance is simple — when in doubt, add more detail.

Step 3: Edit the prompt, not the picture. CNET's testing found that image services "can only handle so many changes before the image becomes too wonky and ultimately unusable." Revising your original prompt gives better odds than stacking edits. One rule from a documented image-generation skill pack is worth memorizing: always specify what to keep unchanged, because the model treats unlisted elements as free to modify.

Step 4: Label the image as AI-created. CNET calls this step extremely important, and it's becoming non-negotiable: note that the image is AI-generated and name the service used, in both captions and alt text. Meta may also flag AI content on Instagram and Facebook.

A practical tip from the same skill pack: batch your work. Generate 10 images, review them side by side, pick one or two, then regenerate with a tighter prompt on the winning direction — it's faster than single-shot iteration.

If you want to automate parts of this workflow, agent skills can do it — SkillzBench is building a free directory and free MCP connector to help you discover, verify, and benchmark image-generation skills before you deploy them.

Level Up: Deploying an Image-Generation Skill Pack to Your AI Agent

Everything you just did manually — picking a model, writing the prompt, generating, optimizing — can be handed to your AI agent as a single instruction package. Agent skills (SKILL.md files that work with Claude Code, Codex, Cursor, and Gemini CLI) turn a multi-step image workflow into something your agent runs on command.

A concrete example is the community ai-image-generator skill pack, which shows exactly how these packages are structured. It opens with YAML frontmatter declaring a name, a description, and allowed tools, then lays out the full pipeline in instructional markdown the agent follows:

• Determine the need — clarify what the image is for before touching any model.
• Build the prompt using its 5-part framework: image type, subject, environment, technical specs, and constraints.
• Generate via API — the skill recommends Python over curl to avoid shell-escaping headaches.
• Optimize the output: convert to WebP and trim excess whitespace.
• Optionally run a QA pass where a vision model critiques the result before you ship it.

The skill pack also encodes practical judgment you'd otherwise learn by trial and error. Its model-selection rule of thumb: any image with readable text goes to GPT Image 2 (unless you need transparency, then GPT 1.5); everything else goes to Gemini. It even bakes in cost awareness — GPT Image 2 pricing runs from $0.006 per 1024×1024 image at low quality to $0.211 at high quality, and Gemini 2.5 Flash Image's free tier allows roughly 500 images per day.

Its editing guidance is equally opinionated. Always specify what to KEEP unchanged, not just what to change — the model treats unlisted elements as free to modify. And rather than single-shot generation plus iteration, the skill prescribes a batch pattern: generate 10, review them side-by-side, pick the best 1–2, then optionally regenerate with a tighter prompt on the winning direction.

One caution before you deploy any community skill: not all of them are safe to install. Published research found that roughly 26% of agent skills carry at least one vulnerability. That's why SkillzBench is building a free directory that security-scans, version-pins, and benchmarks skills — so you can deploy only ones that actually help.

Your First Week: A Concrete Practice Plan

Reading about prompting frameworks is one thing; building the muscle requires a week of deliberate practice. Here's a concrete plan that turns everything above into daily reps, using free tiers so cost is never the excuse.

Days 1–2: Compare two services on the same prompt. Pick one prompt and run it through two free-tier generators. A good starting pair: Gemini 2.5 Flash Image, which offers roughly 500 images per day on its free tier, alongside another service like Canva, which offers 20 free AI uses per month. Note how each interprets your prompt, where text rendering breaks, and how fast results arrive — most services generate images in under two minutes, with faster ones finishing in 10–30 seconds.

Days 3–4: Practice the 5-part framework on a real asset. Take an actual project — a blog header, a social graphic — and build prompts using the structure documented in a community Claude Code skill pack: (1) Image Type, (2) Subject, (3) Environment, (4) Technical Specs, and (5) Constraints. The same skill pack emphasizes narrative prompts over "keyword soup," and stresses the keep-unchanged editing pattern: always specify what should stay the same, because models treat unlisted elements as free to modify. CNET's guidance reinforces this — edit the prompt rather than the picture, since images can only absorb so many changes before becoming unusable.

Day 5: Run a batch and pick winners. Generate 10 images in one session, review them side-by-side, pick one or two, then regenerate with a tighter prompt on the winning direction. According to the skill pack documentation, this batch approach is faster than single-shot iteration.

Before any image ships, run this disclosure checklist:

• Label the image as AI-created, including the service used, in the caption or alt text.
• Check the platform's terms — Meta may flag AI content on Instagram and Facebook.
• Verify commercial licensing, especially for revenue-generating use: Midjourney requires companies earning over $1M in gross annual revenue to be on Pro or Mega plans for commercial use.
• For commercial work, consider services trained on licensed content, such as Adobe Firefly.

Once the manual workflow feels natural, the next step is automating it. Agent skills — instruction packages that let coding agents like Claude Code or Cursor run this entire prompt-generate-optimize pipeline — exist for exactly this purpose, and published research has found that roughly 26% carry at least one vulnerability, so vetting matters. That's the problem SkillzBench is building for: a free directory that verifies, security-scans, and benchmarks skills so you can browse and test free, verified agent skills to automate the rest of this workflow.