gpt-image-cookbook

name: gpt-image-cookbook description: "Use this skill whenever a user asks to generate, create, draw, render, or edit images with AI image models — gpt-image-2, DALL-E, Google Imagen, Flux, or others. Covers text-to-image, reference-image editing, inpainting, posters, typography, UI mockups, diagrams, and curated gallery prompts. Search the bundled cookbook references for matching patterns, confer on direction when ambiguous, then call the packaged gic CLI. Do not write new image-generation code unless the user explicitly asks to modify this repo." compatibility: "Requires Python 3.11+ and one of gic, uv, or uvx. CLI/API calls read provider API keys from env (OPENAI_API_KEY, GOOGLE_API_KEY, FAL_KEY, etc.) and may incur charges on the user's account." metadata: {"openclaw":{"requires":{"anyBins":["gic","uv","uvx"]},"primaryEnv":"OPENAI_API_KEY","homepage":"https://github.com/eugeniughelbur/gpt-image-cookbook"}}

gpt-image-cookbook

Agent runbook for multi-provider AI image generation and editing. Use the prompt cookbook + packaged gic CLI. Do not reimplement image API code.

Operating loop

1. Classify the request: generate, edit, inpaint, or multi-reference. Identify asset type, exact text to render, aspect ratio, references, safety constraints, and budget/quality tier.
2. Pick a provider: default to openai (gpt-image-2). Switch to imagen for Google-native quality on photoreal scenes, or flux for fast/cheap drafts and stylized art. The user's explicit request always wins.
3. Search references first: open references/gallery.md (the routing index). Load the closest references/gallery-<category>.md file(s). Read actual **Prompt** text before choosing a pattern — never guess from category name alone.
4. Refine with craft: load references/craft.md for dense text, diagrams, UI mockups, data visualization, multi-panel layouts, or when the gallery has no close match.
5. Confer when useful: before costly, ambiguous, or high-polish calls, present 1–3 matched directions plus planned size/quality/provider; ask at most one concise question. Skip the discussion for precise "generate now" requests.
6. Preflight, no side effects: check command -v gic first. Do not reinstall, overwrite skill folders, create or modify .env, or write API keys. Global/shared installs are opt-in only.
7. Execute via CLI only: call gic. Do not create a new generate.py, SDK wrapper, or ad-hoc script for normal image requests.
8. Report: output file path(s), the provider/model used, key flags, and one concise refinement suggestion if useful.

Fast path: precise prompt + explicit "generate now" → quick reference/craft check, then CLI.

CLI resolution

Preferred call order:

# Existing CLI on PATH
gic -p "PROMPT" [-f OUT] [-i REF...] [-m MASK] [--provider openai|imagen|flux] [options]

# Repo-local launcher (when the skill folder is bundled with the runtime)
uv run "$SKILL_DIR/scripts/generate.py" -p "PROMPT" [options]

# Transient install when the user requested a one-off CLI execution
uvx --from git+https://github.com/eugeniughelbur/gpt-image-cookbook gic -p "PROMPT" [options]

scripts/generate.py is a launcher: repo-local src/gic → installed gic on PATH → transient uvx fallback.

Provider selection

The CLI resolves the provider from the --provider flag, then GIC_DEFAULT_PROVIDER env var, then falls back to openai.

Key and cost rules

• The CLI reads provider keys from process env, then .env, then ~/.env without overriding existing env. Successful API calls bill the user's provider account.
• If a host runtime has native platform-managed image generation and the user wants that path, use the host tool instead of this CLI.
• If the required key is unset, report the missing key and the env var name; do not write secrets.
• If the user wants to avoid local-key use, respect unset OPENAI_API_KEY (etc.); if a key exists in .env/~/.env, tell them to remove or rename it for the session rather than working around it.
• Never print secret values.

Flags

Quality policy:

• low: cheap drafts, broad exploration, many variants.
• medium: normal exploration, style probing, balanced cost.
• high: final assets, dense text, posters, diagrams, UI mockups, paper figures, dense labels.

Size policy:

• default/social square: 1k / 1024x1024
• poster/mobile/beauty: portrait
• landscape/gameplay/photo: landscape
• print/paper figure: 2k
• widescreen hero: 4k
• vertical story/banner: tall

Endpoint routing

Surface API errors verbatim enough for debugging. Exit codes: 0 success, 1 API/refusal, 2 bad args/missing key.

Reference loading

• references/gallery.md: routing index for the cookbook gallery. Load first.
• references/gallery-*.md: concrete prompts, previews, paths, metadata. Load 1 category for normal requests; 2–3 for hybrids.
• references/craft.md: prompt-craft checklist. Load for prompt repair, exact text rendering, UI/data/diagram grammar, edit invariants, and multi-panel consistency.
• references/providers.md: provider/model semantics. Load for API behavior or capability questions.

Reference loading policy: load the smallest useful slice; never load all category files by default.

Verification

After a generation call:

1. Confirm the output file exists at the reported path.
2. If the prompt requested specific text, verify the text renders correctly — re-run with --quality high if it doesn't.
3. For edits/inpainting, confirm the unmasked regions are preserved.