executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
vision-multimodalOperate AutoTrain Advanced image classification, image regression, object detection, and VLM app/API/config workflows.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill for image classification, image regression/scoring, object detection, and VLM dataset/task flows.
autotrain image-classification --helpautotrain image-regression --helpautotrain object-detection --helpimage-classification, image-regression, image-scoring, object-detection, image-object-detection, vlm:captioning, vlm:vqaimage-classification, image-regression, image-object-detection, vlm:captioning, vlm:vqaImportant: there is no top-level autotrain vlm command in this checkout. Route VLM through the app/API/config flow.
| Task | Local layout |
|---|---|
| image classification | directory with at least two class subfolders; each class folder has at least five jpg/jpeg/png files and no extra files/subfolders |
| image regression | directory with at least five images plus metadata.jsonl containing file_name and target |
| object detection | directory with at least five images plus metadata.jsonl containing file_name and objects |
| VLM | directory with at least five images plus metadata.jsonl containing file_name and all mapped text/prompt columns |
Use scripts/validate_vision_data.py for bounded local layout checks.
inspect_cli.py helper.validate_config.py helper.scripts/validate_vision_data.py.../app-backends/ to inspect app/API task params and backend auth.references/workflows.md — route patterns and launch/check examples.references/data-formats.md — local folder and metadata schemas.references/troubleshooting.md — image file counts, metadata parsing, VLM route, and backend issues.