local-gmncode-vision-pro

Use this skill when basic single-image fallback is not enough and the task needs production-grade image understanding.

Core scripts

• Batch processing:

/home/ubuntu/.openclaw/workspace/skills/local-gmncode-vision-pro/scripts/vision_batch.py

• Structured JSON output:

/home/ubuntu/.openclaw/workspace/skills/local-gmncode-vision-pro/scripts/vision_json.py

Workflow

1. Prefer the built-in image tool if it is healthy and available.
2. If image fails or needs more control, use the Pro scripts.
3. For multi-image work, use vision_batch.py.
4. For agent/tool pipelines, use vision_json.py to get machine-readable output.
5. If results are uncertain, say so explicitly and return best-effort ranked hypotheses.

Dependencies

• Environment variable: GMNCODE_API_KEY
• Model route: gpt-5.4

Read when needed

Read this file for packaging, pricing, and promotion:

/home/ubuntu/.openclaw/workspace/skills/local-gmncode-vision-pro/references-go-to-market.md

Output principles

• Be explicit about uncertainty.
• Separate confirmed observations from inference.
• Prefer structured output for automation.
• Do not overclaim exact character identity when only style-level evidence exists.