chatgpt-image-ad

chatgpt-image-ad (Arcads)

Generate one or more standalone Meta ad image creatives via Arcads' POST /v2/images/generate with model: "gpt-image-2". Hands the image paths off to your Meta-ad-builder skill — this skill does not upload to Meta itself.

Read order

1. This file — Arcads-specific endpoint, auth, presigned upload flow, workflow phases.
2. shared/skills/chatgpt-image-ad/prompting/guide.md — model-specific prompting (what gpt-image-2 is good/bad at, when to switch to nano-banana).
3. shared/skills/image-ad-prompting/prompting/prompt-library.md — 30+ validated templates with per-model notes.
4. shared/skills/image-ad-prompting/prompting/safety-suffixes.md — the 3 always-on guards.
5. scripts/generate_image.py — the helper script (Python stdlib only).

Hard rules — never relax

1. Model is gpt-image-2. The script refuses any other value. If the user asks for nano-banana, point them at nano-banana-image-ad.
2. No platform/screenshot chrome in output. NO_CHROME_SUFFIX is always on (override with --allow-chrome only when the ad's concept requires chrome — rare).
3. Edge-safe + glyph-safety suffixes always on unless --no-safe-zone is explicit. They fix real failures; don't remove silently.
4. Max 5 reference images. Hard Arcads cap for gpt-image-2 (observed 400 Max 5 reference image(s) allowed). The script enforces.
5. No Meta upload from this skill. Image generation only. The user has a separate ad-builder skill in their stack — hand off via filesystem paths.
6. Always present a credit-cost estimate before generating (see Arcads arcads-external-api skill conventions). Each gpt-image-2 call is one image; multiply by --n variants.

Prerequisites

• .env containing ARCADS_BASIC_AUTH (preferred, pre-encoded) OR ARCADS_API_KEY
• Optional: PRODUCT_ID, PROJECT_ID in .env so generated assets land in the right Arcads dashboard folder (see arcads-external-api SKILL.md for the session-folder pattern)
• Reference images on local disk (PNG/JPG/JPEG/WEBP/GIF). The script handles the Arcads presigned-upload flow internally — you pass local paths.

Configuration

• Base URL: https://external-api.arcads.ai (or ARCADS_BASE_URL).
• Auth: HTTP Basic. The script prefers a pre-encoded ARCADS_BASIC_AUTH env var (e.g. Basic ZXhhbXBsZTo=); falls back to encoding ARCADS_API_KEY with an empty password.
• Endpoint: POST /v2/images/generate (request); GET /v1/assets/{id} (poll until status: generated); image URL fetched once status flips.
• Reference uploads: POST /v1/file-upload/get-presigned-url returns {presignedUrl, filePath}; PUT the bytes to presignedUrl; pass the filePath string in referenceImages. Each filePath is single-use — the script re-uploads per variant so parallel runs don't collide.

Generation modes

Supported aspect ratios

1:1, 16:9, 9:16. Only these three are accepted by Arcads' /v2/images/generate endpoint (confirmed live: aspectRatio must be one of the following values: 1:1, 16:9, 9:16). Templates in the shared library that use 2:3, 3:2, 4:5, etc. won't render at their native ratio on this backend — fall back to 1:1 and crop, or use the KIE chatgpt-image-ad sibling which supports 2:3 and 3:2 natively via its dedicated /gpt4o-image/generate endpoint.

Inputs the user must provide

Workflow

Phase 1: Preflight

1. .env exists with ARCADS_BASIC_AUTH or ARCADS_API_KEY.
2. (Optional) arcads-external-api session folder is set up (see that skill's "Session setup" section). If PRODUCT_ID / PROJECT_ID aren't set, generated assets land in the default project — you can fix later via POST /v1/assets/add-to-project.
3. Health-check: curl -sf -H "$AUTH" "$BASE_URL/v1/products" returns 200.

Phase 2: Gather inputs

Collect: seed prompt, mode, source (if edit), reference paths, variant count, aspect ratio.

Phase 3: Prompt rewrite

Read shared/skills/image-ad-prompting/prompting/prompt-library.md. If the user's brief maps onto a template, check the Model notes block — only proceed if gpt-image-2 is marked clean or preferred. If nano-banana is preferred, suggest switching skills before generating.

Fill the {placeholders} and show the user the rewritten prompt. Ask "Use this, edit it, or start over?" Loop until approved.

For fresh prompts (no template match), follow the structure in shared/skills/chatgpt-image-ad/prompting/guide.md § Phase 3b.

Phase 4: Credit cost confirmation (MANDATORY)

Per arcads-external-api conventions: present an estimated credit cost (read from logs/arcads-api.jsonl for matching past calls, or MASTER_CONTEXT.md rate table). Wait for explicit confirmation before firing.

Phase 5: Generate

~/.claude/skills/chatgpt-image-ad/scripts/generate_image.py \
  --prompt "<rewritten>" \
  --aspect-ratio <ratio> \
  --n <N> \
  --image-ref <product.png> \
  [--image-ref <style-board.png>] \
  --out ./generated \
  --env-file .env

# For an edit run:
~/.claude/skills/chatgpt-image-ad/scripts/generate_image.py \
  --mode image_edit \
  --prompt "<edit-instruction>" \
  --source <existing.png> \
  [--image-ref <guidance.png>] \
  --n <N> \
  --out ./generated \
  --env-file .env

Each line on stdout is one JSON variant (variant, path, asset_id, width, height, prompt, mode, aspect_ratio, model).

Log each call to logs/arcads-api.jsonl with model=gpt-image-2, the variant count, referenceImages count, and the returned asset_ids, per arcads-external-api skill conventions.

Phase 6: Visual QA (MANDATORY)

For each completed variant, read the image and inspect for:

• Garbled small text (most common gpt-image-2 failure on dense body text)
• Wordmark drift (if a brand wordmark wasn't passed as --image-ref)
• Wrong text count (e.g. 4 Slack messages instead of 3)
• iOS dialog / UI proportion drift

If defects: regenerate with a revised prompt explicitly correcting the issue (see shared/skills/chatgpt-image-ad/prompting/guide.md § Retry mode). Cap at 2 retries per variant.

Phase 7: Confirm and hand off

Show all paths to the user. Ask "Use all / use these specific ones / regenerate / cancel."

Selected variants are now ready for your Meta-ad-builder skill (the separate skill that handles cloning, copy, and upload). Print the paths so the user can pipe them.

Optionally, write the selected paths to ./generated/run-<ts>.jsonl (one path per line, JSON-wrapped) for downstream consumption.

Out of scope — fail clearly

• Meta upload — different skill in your stack.
• Nano Banana / Gemini image generation — use nano-banana-image-ad.
• Video, carousel, DCO ads — image only.
• Ad copy writing — different skill.
• Editing the shared prompt library — use image-ad-clone (asks which backend at Phase 1).

Common errors

• 401/403 → fix .env per arcads-external-api setup flow.
• 400 Max 5 reference image(s) allowed → reduce --image-ref count to ≤5.
• 422 validation/moderation → tighten the prompt; check that aspectRatio is in the supported set.
• 500 UNKNOWN_ERROR → usually a stale presigned filePath being reused. The script re-uploads per variant; if you still see this, file an issue with the asset_id from the response.

Files this skill owns

• ~/.claude/skills/chatgpt-image-ad/SKILL.md — this file
• ~/.claude/skills/chatgpt-image-ad/scripts/generate_image.py — Arcads gpt-image-2 caller (presigned upload + generate + poll + download)

See also

• shared/skills/chatgpt-image-ad/prompting/guide.md — model-specific prompting (gpt-image-2 strengths/limits, when to switch to nano-banana)
• shared/skills/image-ad-prompting/prompting/prompt-library.md — shared template library (30+ entries with per-model notes)
• shared/skills/image-ad-prompting/prompting/template-format.md — entry skeleton for new templates
• shared/skills/image-ad-prompting/prompting/safety-suffixes.md — the 3 always-on guards
• image-ad-clone skill — single backend-agnostic skill that reverse-engineers an existing ad into a reusable library entry
• arcads-external-api skill — underlying Arcads conventions (session folder, credit cost, QA loop, logs)
• nano-banana-image-ad skill — sibling skill for photoreal / lifestyle / multi-ref templates