gpt-image
Use this skill whenever a user asks to generate, create, draw, render, or edit images with GPT Image 2 / gpt-image-2, text-to-image, reference-image editing, inpainting, posters, typography, Chinese text, UI mockups, diagrams, or gallery prompts. Analyze the user's prompt, search the bundled Reference Gallery/craft files for matching design patterns, confer on direction when useful, then call the packaged `gpt-image` CLI or bundled `scripts/generate.py`. Do not write new image-generation code unless explicitly asked to modify this repo.
npx skills add wuyoscar/GPT-Image2-Skill --skill gpt-image --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# gpt-image Agent runbook for GPT Image 2 generation/editing. Use the prompt library + packaged CLI. Do not reimplement image API code. ## Operating loop 1. **Classify request**: `generate`, `edit`, `inpaint`, or `multi-reference`; identify asset type, exact text, aspect ratio, references, safety constraints, and budget/quality. 2. **Search references first**: open `references/gallery.md`; load/search the closest `references/gallery-<category>.md` file(s). Read actual `**Prompt**` text before choosing a pattern. 3. **Refine with craft**: load `references/craft.md` for dense text, diagrams, UI, data visualization, multi-panel layouts, weak prompts, or no close gallery match. 4. **Confer when useful**: before costly/ambiguous/high-polish calls, present 1–3 matched directions plus planned size/quality; ask at most one concise question. Skip long discussion for precise “generate now” requests. 5. **Preflight, no side effects**: use existing CLI/skill if present. Check command availability (`command -v gpt-image`), installed tool lists when the tool manager exists, or the runtime’s own skill registry when available. Do not assume a local home path in cloud/hosted runtimes. 6. **No blind
- Operating loop
- CLI resolution
- Key and cost rules
- Flags
- Endpoint routing
- Reference loading
- Verification
Existing CLI on PATH gpt-image -p "PROMPT" [-f OUT] [-i REF...] [-m MASK] [options] Installed skill folder; use runtime-provided skill path when available uv run "$SKILL_DIR/scripts/generate.py" -p "PROMPT" [-f OUT] [-i REF...] [-m MASK] [options] Direct transient CLI when the user requested setup/one-off CLI execution uvx --from git+https://github.com/wuyoscar/gpt_image_2_skill gpt-image -p "PROMPT" [options]
What does the gpt-image skill do?
Use this skill whenever a user asks to generate, create, draw, render, or edit images with GPT Image 2 / gpt-image-2, text-to-image, reference-image editing, inpainting, posters, typography, Chinese text, UI mockups, diagrams, or gallery prompts. Analyze the user's prompt, search the bundled Reference Gallery/craft files for matching design patterns, confer on direction when useful, then call the packaged `gpt-image` CLI or bundled `scripts/generate.py`. Do not write new image-generation code unless explicitly asked to modify this repo.
How do I install it?
Run `npx skills add wuyoscar/GPT-Image2-Skill --skill gpt-image --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From wuyoscar/GPT-Image2-Skill, a repository with 4,147 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.