GPT Image 2 — High-quality AI image generation

Powered by ClawdChat — calls OpenAI gpt-image-2 through the Uno tool gateway.

What this skill does

Two thin command-line invocations against the public ClawdChat tool gateway:

This skill ships no local Python code. It defers all credential, transport and rate-limit handling to the uno-cli companion skill.

Credentials & permissions (please read before first use)

• Credential type: ClawdChat API key (Bearer token).
• Where it lives: ~/.clawdchat/credentials.json. The file is created and owned by the uno-cli skill; this skill never opens, prints or copies it.
• How it was obtained: the user runs uno login in uno-cli, which opens an OAuth flow on https://clawdchat.cn and stores the resulting token.
• What it authorises: calling the ClawdChat tool gateway as the logged-in user. Each gpt_image2_submit deducts 300 credits from that account.
• Network egress: the user's prompt text and any reference_image_urls are sent to the ClawdChat gateway over HTTPS. Do not paste private, confidential, or personally-identifying content into the prompt unless you are comfortable with the gateway's data handling — see https://clawdchat.cn for the data policy.
• Logging out / revoking: run uno logout (managed by uno-cli).

Cost transparency & confirmation rule

Every gpt_image2_submit call costs the logged-in account real credits. The agent must:

1. Show the user the planned prompt, size, style, and number of images before the first call.
2. Ask for explicit confirmation when the user has not already approved a generation in the current turn.
3. For multi-image batches (n > 1) or retries, treat each submission as a separate spending event and confirm again unless the user has pre-authorised the batch.
4. On error responses, surface the error to the user instead of silently retrying.

Polling via gpt_image2_result is free; only submit spends credits.

Setup

This skill depends on the uno-cli skill (declared in metadata.openclaw.skills).

1. Install uno-cli (skipping if already installed):

   clawhub install uno-cli
   

   On platforms that honour metadata.openclaw.skills, this dependency is installed automatically when this skill is installed.

2. Log in once:

   uno login
   

   The login flow, credential storage, and refresh are entirely handled by uno-cli. This skill only invokes uno call ... afterwards.

If uno is not on PATH, replace it in the examples below with python /path/to/uno-cli/bin/uno.py.

Generating an image — full async flow

A single 1024×1024 image typically takes ~150 s, longer than the default MCP 60 s timeout. Always use the submit → poll-result pattern.

Step 1 — submit

uno call gpt-image-2.gpt_image2_submit --compact \
  --args '{"prompt":"A shiba inu under cherry blossoms, sunny afternoon","size":"1024x1024","style":"ghibli_anime"}'

Response (already flattened by uno-cli — no need to unwrap content[0].text):

{"success": true, "data": {"status": "pending", "job_id": "0b84b8f0f0c8", "estimated_seconds": 150}, "meta": {"latency_ms": 120, "credits_used": 300}}

Record data.job_id.

Step 2 — poll for result

uno call gpt-image-2.gpt_image2_result --compact --timeout 70 \
  --args '{"job_id":"0b84b8f0f0c8","wait_seconds":50}'

wait_seconds=50 makes the server-side wait 50 s (within the 60 s MCP envelope); --timeout 70 adds a small client buffer.

Repeat the call until data.status is one of:

• done — image ready, URLs in data.items[].url.
• error — generation failed, message in data.error.
• pending / running — call again immediately. Do not add a client-side sleep; the server already waited 50 s on your behalf.

Three to five iterations (~150–250 s total) is normal.

Reference shell loop

RESP=$(uno call gpt-image-2.gpt_image2_submit --compact \
  --args '{"prompt":"Van Gogh starry night","style":"oil_painting_vangogh"}')
JOB_ID=$(echo "$RESP" | python3 -c "import json,sys; print(json.load(sys.stdin)['data']['job_id'])")

for i in 1 2 3 4 5 6; do
  R=$(uno call gpt-image-2.gpt_image2_result --compact --timeout 70 \
    --args "{\"job_id\":\"$JOB_ID\",\"wait_seconds\":50}")
  STATUS=$(echo "$R" | python3 -c "import json,sys; print(json.load(sys.stdin)['data']['status'])")
  [ "$STATUS" = "done" ]  && echo "$R" && break
  [ "$STATUS" = "error" ] && echo "$R" && exit 1
done

Parameters

20 built-in style presets

Where this model shines (vs Midjourney / Flux / SD)

• Accurate text rendering — poster headlines, infographics, menu typography, meme captions: written into the image as specified.
• Strong prompt following — multi-element scenes, ordering and spatial relationships obeyed.
• Subject preservation in image-to-image — faces, brands, and characters stay consistent across reference images.
• Wide style coverage — Ghibli, Pixar, claymation, LEGO, Pop Mart, botanical engraving etc. all handled.

Agent guidance

• Tell the user up-front that one image takes ~150 s.
• The gpt_image2_result tool already sleeps 50 s server-side — never add an extra client-side sleep between polls.
• Use --timeout 70 for result calls (50 s server wait + buffer).
• Pass the user's prompt verbatim, including non-English text.
• Reference images: combine reference_image_urls with a style preset for "restyle while keeping the subject".
• Posters / infographics / menus: lean on the text-rendering strength.
• If submit returns success=false, surface the error/hint fields to the user.
• If the loop exhausts (~600 s) and status is still running, tell the user the job can be re-polled later with the same job_id.

Response shape

Already flattened by uno-cli:

{
  "success": true,
  "data": {"status": "...", "job_id": "...", "items": [{"url": "..."}]},
  "meta": {"latency_ms": 120, "credits_used": 300}
}

Read data.status, data.job_id, data.items[].url directly.

Errors:

{"success": false, "error": "...", "hint": "..."}