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 jezweb/claude-skills --skill ai-image-generatorgit clone --depth 1 https://github.com/jezweb/claude-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/jezweb/claude-skills/ai-image-generator)<a href="https://agentmods.dev/skills/jezweb/claude-skills/ai-image-generator"><img src="https://agentmods.dev/badge/skills/jezweb/claude-skills/ai-image-generator/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/jezweb/claude-skills/ai-image-generator"><img src="https://agentmods.dev/badge/skills/jezweb/claude-skills/ai-image-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
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 | $0.00165 | $0.03960 |
| Opus 5 | $0.00082 | $0.01980 |
| Sonnet 5 | $0.00033 | $0.00792 |
| Haiku 4.5 | $0.00016 | $0.00396 |
Grade C, and why
ai-image-generator scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -s "https://generativelanguage.googleapis.com/v1beta/models?key=$GEMINI_API_KEY" | python3 -c "import sys,json; [print(m['name']) for m in json.load(sys.stdin)['models'] if 'image' in m['name'].lower()]" Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://generativelanguage.googleapis.com/v1beta/models?key=$GEMINI_API_KEY" | python3 -c "import sys,json; [print(m['name']) for m in json.load(sys.stdin)['models'] if 'image' in m['name'].lower()]" How it starts
The opening of the file, as written. The whole thing — 390 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Image Generator
Generate images using AI APIs (Google Gemini and OpenAI GPT). This skill teaches the prompting patterns and API mechanics for producing professional images directly from Claude Code.
Managed alternative: If you don't want to manage API keys, ImageBot provides a managed image generation service with album templates and brand kit support.
Model Selection
Choose the right model for the job:
| Need | Model | Why |
|---|---|---|
| Photorealistic scenes / stock photos | Gemini 3.1 Flash Image | Best depth, complexity, environmental context |
| Final client scenes (higher detail) | Gemini 3 Pro Image | Higher detail, better style consistency |
| Text on images (posters, OG with copy, infographics) | GPT Image 2 | Text rendering actually works — including multi-script |
| 10-variation style exploration | GPT Image 2 | Native batch — one prompt, 10 variants sharing composition + palette |
| Multi-reference compositing (product + lifestyle) | GPT Image 2 | Handles lighting, scale, perspective across references |
| Transparent icons / logos | GPT Image 1.5 | Native RGBA alpha — GPT Image 2 cannot do transparency |
| Quick drafts / iteration | Gemini 2.5 Flash Image | Free tier (~500/day) |
Rule of thumb: any image with readable text → GPT Image 2 (unless you need transparency, then GPT 1.5). Otherwise → Gemini.
Model IDs
| Model | API ID | Provider |
|---|---|---|
| Gemini 3.1 Flash Image | gemini-3.1-flash-image-preview |
Google AI |
| Gemini 3 Pro Image | gemini-3-pro-image-preview |
Google AI |
| Gemini 2.5 Flash Image | gemini-2.5-flash-image |
Google AI |
| GPT Image 2 (default) | gpt-image-2 |
OpenAI |
| GPT Image 2 (ChatGPT-parity output) | chatgpt-image-latest |
OpenAI |
| GPT Image 1.5 (transparency-only) | gpt-image-1.5 |
OpenAI |
Verify model IDs before use — they change frequently:
curl -s "https://generativelanguage.googleapis.com/v1beta/models?key=$GEMINI_API_KEY" | python3 -c "import sys,json; [print(m['name']) for m in json.load(sys.stdin)['models'] if 'image' in m['name'].lower()]"
What ships with it
1 file 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.
- 13d ago First seen · 390 lines · 165 tokens per session scan C c6dfa3c1153c
ai-image-generator is a skill published in the GitHub repository jezweb/claude-skills (1,001 stars, last pushed 2mo ago), licensed MIT. It adds 165 tokens to every session and 3,960 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
ensemble-solving
Generate multiple diverse solutions in parallel and select the best. Use for architecture decisions, code generation with multiple valid approaches, or creative tasks where exploring alternatives improves quality.
code-transfer
Transfer code between files with line-based precision. Use when users request copying code from one location to another, moving functions or classes between files, extracting code blocks, or inserting code at specific line numbers.
feature-planning
Break down feature requests into detailed, implementable plans with clear tasks. Use when user requests a new feature, enhancement, or complex change.
review-implementing
Process and implement code review feedback systematically. Use when user provides reviewer comments, PR feedback, code review notes, or asks to implement suggestions from reviews.
codebase-documenter
Generates comprehensive documentation explaining how a codebase works, including architecture, key components, data flow, and development guidelines. Use when user wants to understand unfamiliar code, create onboarding docs, document architecture, or explain how the system works.
conversation-analyzer
Analyzes your Claude Code conversation history to identify patterns, common mistakes, and opportunities for workflow improvement. Use when user wants to understand usage patterns, optimize workflow, identify automation opportunities, or check if they're following best practices.