gpt-image

Agent runbook for GPT Image 2 generation/editing. Use the prompt library + packaged CLI. Do not reimplement image API code.

Operating loop

Classify request: generate, edit, inpaint, or multi-reference; identify asset type, exact text, aspect ratio, references, safety constraints, and budget/quality.
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.
Refine with craft: load references/craft.md for dense text, diagrams, UI, data visualization, multi-panel layouts, weak prompts, or no close gallery match.
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.
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.
No blind setup: do not reinstall, overwrite skill folders, create/modify .env, or write API keys unless the user explicitly requested setup. Global/shared installs are opt-in only.
Execute via CLI only: call gpt-image or scripts/generate.py. Do not create a new generate.py, SDK wrapper, or ad-hoc script for normal image requests.
Report: output file path(s), key flags, and one concise refinement suggestion if useful.

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