local-gmncode-vision-pro

Advanced local vision infrastructure for agents when built-in image tools are unavailable or unreliable. Use for batch image analysis, structured JSON output, screenshot/UI understanding, character/style classification, retryable workflows, and resilient image-processing pipelines backed by GMNCODE. Trigger when a user wants a paid/professional-grade version of local-gmncode-vision, production-ready visual automation, or reusable vision infrastructure for teams and agent systems.

作者: admin | 来源: ClawHub

# 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.