gpt-image-2-skill（gpt 图像 2 技能）

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 → installed binary → Tauri App bundled CLI → repo cargo run → cached release → bootstrap download) and forwards every flag.

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 '#00ff00' --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.

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:

The CLI is intentionally not a material classifier. The Agent should choose generation prompts and extraction methods based on the asset:

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 magenta \
  --out /tmp/necklace.png --spill-suppression 0.85 --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, 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.