Gpt Image2

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:

| Tool slug | Purpose | Cost | |---|---|---| | gpt-image-2.gpt_image2_submit | Submit a generation job, returns job_id immediately (async) | 300 credits / call | | gpt-image-2.gpt_image2_result | Poll job status / fetch image URL when ready | 0 credits |

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)

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:

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

| Field | Meaning | Values | |---|---|---| | prompt | Image description (required, any language) | free text | | size | Image dimensions | 1024x1024 (default), 1024x1536 (portrait), 1536x1024 (landscape), auto | | n | Number of images to generate | 1–4 (default 1) | | style | Built-in style preset | one of the 20 keys below | | reference_image_urls | Reference images (image-to-image) | URL string, comma-separated for multiple |

20 built-in style presets

| key | description | |---|---| | ghibli_anime | Studio Ghibli / hand-drawn anime | | pixar_3d | Pixar / Disney 3D animation | | claymation | Stop-motion claymation (Laika / Aardman) | | lego_brick | LEGO bricks | | popmart_figurine | Blind-box / Pop Mart figurine | | isometric_game | Isometric 2.5D game scene | | cinematic_photo | Cinematic photorealism (35mm) | | polaroid_film | Polaroid film snapshot | | watercolor_ink | Watercolour / East-Asian ink wash | | oil_painting_vangogh | Van Gogh impasto oil painting | | cyberpunk_neon | Cyberpunk neon nightscape | | vintage_infographic | Retro infographic / data poster | | movie_poster | Movie poster (large title + still) | | flat_vector | Flat-vector illustration / banner | | pixel_8bit | Pixel art (8/16-bit) | | papercraft_layered | Layered papercraft | | exploded_diagram | Exploded technical diagram | | dreamcore_liminal | Dreamcore / liminal space | | knolling_flatlay | Top-down knolling / flat-lay | | botanical_engraving | Botanical engraving / antique illustration |

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

Agent guidance

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": "..."}

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.