gpt-image-2-skill

name gpt-image-2-skill description This skill should be used when the user asks to "generate an image", "create a logo", "draw an icon", "edit this photo", "change background to transparent", "remove background", "use GPT image", "use Codex to draw", "用 GPT image 生成图片", "用 Codex 画图", "帮我生成一张图", "改成透明背景", "把这张图编辑一下", or any prompt-to-image or reference-image-edit task that benefits from a structured CLI returning JSON results and JSONL progress events. Supports OpenAI `gpt-image-2` (via `OPENAI_API_KEY` or OpenAI-compatible base URL) and Codex `image_generation` (via `~/.codex/auth.json`) under one command surface, with masks, custom sizes up to 4K, transparent backgrounds, and a raw request escape hatch. Run image generation and editing through one CLI surface that hides provider differences. The Node wrapper at scripts/gpt_image_2_skill.cjs resolves an underlying Rust binary (env override → bundled Skill binary → installed binary → Tauri App bundled CLI → repo cargo run → cached release → bootstrap download) and forwards every flag. On glibc Linux, release bootstrap tries the GNU archive first and then the static musl archive as the sandbox fallback. When to use this skill Generate or edit an image and capture a structured result an agent can parse. Switch between OPENAI_API_KEY , an OpenAI-compatible base URL, and Codex auth.json without changing command shape. Respect shared provider config at $CODEX_HOME/gpt-image-2-skill/config.json so CLI, App, and Skill use the same default provider. Need final transparent PNG deliverables, masks, custom sizes up to 4K, or raw request bodies. Want live progress events (retries, multipart prep, Codex SSE) on stderr while the final JSON lands on stdout. Quick start Always pass --json so the result is machine-readable. Add --json-events when progress visibility matters. # 1. Confirm runtime + provider readiness node scripts/gpt_image_2_skill.cjs --json config inspect node scripts/gpt_image_2_skill.cjs --json doctor node scripts/gpt_image_2_skill.cjs --json auth inspect # 2. Generate a final transparent PNG deliverable node scripts/gpt_image_2_skill.cjs --json --json-events \ transparent generate --prompt "..." --out /tmp/asset.png \ --size 2K --quality high # 3. Generate a normal image (auto-selects provider; OpenAI first, then Codex) node scripts/gpt_image_2_skill.cjs --json --json-events \ images generate --prompt "..." --out /tmp/out.png \ --format png --size 2K # 4. Edit a reference image (OpenAI multipart) node scripts/gpt_image_2_skill.cjs --json --json-events \ images edit --prompt "..." --ref-image /tmp/in.png --out /tmp/out.png # 5. Remove a controlled background from existing source images node scripts/gpt_image_2_skill.cjs --json \ transparent extract --input /tmp/source-green.png --out /tmp/asset.png \ --method chroma --matte-color auto --strict # 6. Verify the final file before delivery node scripts/gpt_image_2_skill.cjs --json \ transparent verify --input /tmp/asset.png --profile icon --strict # 7. Raw request escape hatch node scripts/gpt_image_2_skill.cjs --json \ request create --request-operation generate \ --body-file /tmp/body.json --out-image /tmp/out.png --expect-image # 8. Self-test (calls doctor + auth inspect) node scripts/selftest.cjs Force a provider with --provider openai , --provider codex , or any named provider from config inspect ; leave the default --provider auto to use default_provider first. Override the legacy OpenAI base URL with --openai-api-base https://... . Runtime freshness check Before using newly documented command groups, especially transparent generate , transparent extract , or transparent verify , confirm the actual CLI runtime is fresh enough. Skill files, the Tauri App bundle, npm global CLI, cargo-installed CLI, and wrapper cache can drift independently. Start by checking the latest upstream CLI version: npm view gpt-image-2-skill version Then look for the Tauri App bundled CLI first. Treat the app bundle as user-managed software: if it exists but is older than upstream, ask the user before updating or replacing the app. APP_CLI= "/Applications/GPT Image 2.app/Contents/Resources/bin/gpt-image-2-skill" [ -x " $APP_CLI " ] && " $APP_CLI " --version If no app bundle CLI exists, check the shell CLI. If it is missing or older than upstream, silently update the CLI dependency with npm, then verify that the current shell resolves the new binary: command -v gpt-image-2-skill || true gpt-image-2-skill --version || true npm install -g gpt-image-2-skill@latest command -v gpt-image-2-skill gpt-image-2-skill --version After any update, run the skill wrapper smoke checks: node scripts/gpt_image_2_skill.cjs --json doctor node scripts/selftest.cjs If a documented subcommand fails with unrecognized subcommand , diagnose stale runtime first rather than changing prompts or providers. Prefer the wrapper in this skill directory for reproducible skill execution, but keep the bare CLI fresh when examples or user commands call gpt-image-2-skill directly. Shared config Use the CLI config surface when the user asks to add or pin a provider: node scripts/gpt_image_2_skill.cjs --json config path node scripts/gpt_image_2_skill.cjs --json config add-provider \ --name my-image-api \ -- type openai-compatible \ --api-base https://example.com/v1 \ --api-key sk-... \ --set-default node scripts/gpt_image_2_skill.cjs --json config test-provider my-image-api Credential sources supported by CLI, App, and Skill: file , env , and keychain . File credentials are stored in the shared config file; JSON output redacts them. Flags vs prompt — what each controls Output properties (not "what to draw") are flag-controlled. Putting them in the prompt is unreliable and provider-dependent. Property Use this flag, not the prompt Output background (transparent / opaque / auto) --background auto|transparent|opaque Output dimensions --size 2K , --size 4K , or --size WIDTHxHEIGHT Output container --format png|jpeg|webp Compression level --compression 0..100 Render quality --quality low|medium|high|auto Number of images --n <count> (OpenAI only) Edit mask region --mask <png> (OpenAI only) The prompt is for "what is in the picture"; background, size, format, count, and mask are not. For example, to turn a transparent PNG into a white-background PNG, pass --background opaque — describing "white background" only in the prompt is not reliable . Provider asymmetry : --background , --n , --moderation , --mask , and --input-fidelity are honored only by OpenAI (and OpenAI-compatible bases that proxy them). Codex image_generation does not honor --background ; the runtime accepts the flag but the upstream tool drops it. The other four return code: "unsupported_option" if passed with --provider codex . Transparent PNG deliverables For transparent output, do not rely on provider-native transparency. Use the transparent command group as the Agent-facing tool layer: transparent generate — prompt-to-final PNG. It generates a controlled matte source, extracts alpha locally, verifies the result, and only succeeds when the final PNG passes transparency checks. transparent extract — local background removal from controlled source images you generated yourself. It is not a general-purpose background remover for arbitrary photos. transparent verify — final gate for any PNG before delivery. Use --strict and the right --profile when the file must be accepted or fail the task. A transparent deliverable is valid only if the final file has a real PNG alpha channel and passes verification. A visual appearance of transparency, a white background, or a checkerboard pattern is not sufficient. --strict is profile-based: Profile Use for Extra strictness generic common alpha/file checks does not over-police unusual assets icon clean single-subject icons and props requires clean opaque core, margin, low stray noise product product/object cutouts similar to icon, with residue and edge checks sticker decals, badges, multi-detail props allows more intentional small components than icon seal stamps, seals, logos with inner marks allows split components such as ring + center symbol translucent glass, liquid, crystal requires partial alpha glow light ribbons, flame, smoke, particles requires partial alpha and transparent margin shadow soft shadow assets requires partial alpha and transparent margin effect hard-alpha particles, bursts, UI effects transparent margin without requiring partial alpha The CLI is intentionally not a material classifier. The Agent should choose generation prompts and extraction methods based on the asset: Asset type Generation guidance Extraction guidance Opaque object, icon, sticker, product Single isolated subject, clear margin, perfectly flat chroma matte. Pick a matte color absent from the object. transparent generate or transparent extract --method chroma --matte-color auto Thin edges, hair, fur, lace, chain, netting Use high resolution, strong subject/background contrast, no contact shadow, no background-colored details. Try magenta/cyan/green mattes if one contaminates the edge. Chroma extraction with --spill-suppression when needed, then verify with --expected-matte-color ; retry with a different matte if residue remains. Glass, crystal, liquid, hologram Ask for a centered asset on flat black and flat white backgrounds, keeping geometry identical. Use reference/edit flow when possible to keep alignment. transparent extract --method dual --dark-image black.png --light-image white.png Glow, flame, smoke, mist, magic particles Generate dark and light background variants. Avoid textured backgrounds and avoid bloom reaching the image edge unless the edge is intentional. Prefer dual extraction; verify that partial_pixels is non-zero. Shadows Decide whether the shadow is part of the asset. If not, explicitly forbid contact shadows. If yes, generate on a flat matte with enough margin. Chroma for opaque shadow silhouettes; dual extraction for soft translucent shadows. Unknown or unusual material Do not classify it first. Generate controlled source variants, run extraction candidates, and keep the one that passes verification with the cleanest edge. Use --report-dir / --keep-sources while iterating, then deliver only the final PNG. For chroma extraction, --matte-color auto samples the actual flat source background from the image edges. Prefer it when the source was AI-generated, because prompts like "pure #ff00ff" often produce near-matte colors rather than exact RGB values. Use explicit --matte-color <name|#rrggbb> only when the source background is known exactly. For extraction tuning, use --material only as a broad hint, not as a subject classifier: standard , soft-3d , flat-icon , sticker , or glow . Manual --threshold , --softness , and --spill-suppression override the selected preset. For style-locked transparent assets, transparent generate is prompt-only. Use a flat RGB reference image with images edit --ref-image to create a controlled matte source, then run transparent extract . Do not use a transparent PNG as the reference image unless you intentionally want the alpha/composited edge behavior to influence the edit. GPT Image 2 can render accurate UI text, numbers, scores, labels, and logo marks in the bitmap when they are part of the desired artwork. Put the exact wording or numbering in the prompt and verify the output visually. Render text separately in the host app or design tool only when it must stay editable, localizable, programmatically changeable, or perfectly consistent across many generated variants. Examples: # Simple asset: final transparent PNG, sources hidden unless there is a failure node scripts/gpt_image_2_skill.cjs --json --json-events \ transparent generate \ --prompt "a polished fantasy sword game asset, no text, no frame" \ --out /tmp/sword.png --size 2K --quality high # Agent-controlled chroma flow node scripts/gpt_image_2_skill.cjs --json --json-events \ images generate \ --prompt "a silver necklace, centered, on a perfectly flat pure magenta background, no shadow" \ --out /tmp/necklace-magenta.png --format png --size 2K node scripts/gpt_image_2_skill.cjs --json \ transparent extract --method chroma \ --input /tmp/necklace-magenta.png --matte-color auto \ --out /tmp/necklace.png --material sticker --strict # Semi-transparent material flow node scripts/gpt_image_2_skill.cjs --json \ transparent extract --method dual \ --dark-image /tmp/glow-on-black.png \ --light-image /tmp/glow-on-white.png \ --out /tmp/glow.png --strict Always inspect the JSON verification fields before delivery: passed , alpha_min , alpha_max , transparent_ratio , partial_pixels , and warnings . Also inspect quality fields: checkerboard_detected , touches_edge , edge_margin_px , stray_pixel_count , largest_component_ratio , matte_residue_checked , matte_residue_score , halo_score , transparent_rgb_scrubbed , alpha_health_score , residue_score , quality_score , and failure_reasons . If passed is false, do not deliver the file as a transparent PNG. If matte_residue_checked is false for a chroma-derived PNG, run transparent verify again with the source matte via --expected-matte-color . Notes openai defaults to gpt-image-2 ; codex defaults to gpt-5.4 and delegates to image_generation . Shared options actually honored everywhere: --size , --quality , --format , --compression . OpenAI-only options: --background , --n , --moderation , --mask , --input-fidelity . Retries: up to 3 with exponential backoff (1s → 2s → 4s). Codex 401 triggers one token refresh + one retry. Size aliases: 2K → 2048x2048 , 4K → 3840x2160 . Custom WxH requires both edges multiples of 16, max edge 3840, max 8,294,400 pixels, max aspect ratio 3:1. Reference files Load on demand for deeper detail: references/providers.md — OpenAI / OpenAI-compatible / Codex selection, auth sources, runtime discovery, update policy, and resolution order. references/sizes-and-formats.md — size aliases, custom constraints, format/quality/compression/background, shared vs OpenAI-only flags. references/transparent-png.md — Agent playbook for prompt design, controlled mattes, dual-background extraction, verification, and retry loops. references/json-output.md — --json stdout schema, success and error envelopes, per-command shapes. references/json-events.md — --json-events JSONL phases ( request_started , multipart_prepared , retry_scheduled ) and Codex SSE passthrough. references/troubleshooting.md — runtime_unavailable , auth_missing , Codex 401 refresh, retry policy, size rejections, moderation, timeouts. Codex compatibility The companion file agents/openai.yaml is read by Codex Skill runtime only (Claude Code ignores it). Both runtimes execute the commands above with cwd at the skill directory, so relative paths like scripts/gpt_image_2_skill.cjs resolve in either harness.