gpt-image-2-skill

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.