au-fuel-sign-ocr-factory

Manages the flow of scraped fuel sign images from data/ingest/ into the labeling pipeline at data/tmp/. Handles dedup (by filename and content hash), manifest registration, and ingest cleanup. Use this skill when the user mentions "process ingest", "check ingest", "new images", "move scraped images", "run pipeline", "ingest to tmp", or asks about images waiting to be processed.

Stage 1 Labeler: Annotates sign_board bounding boxes on full camera photos for Finder training. Find the physical sign panel in the photo and draw ONE bbox per image. Use this skill when the user wants to label images for the Finder model.

Plans and executes scrape campaigns for Australian fuel station price sign images. Assesses current dataset state, brand gaps, and historical yield. Use this skill when the user wants to "scrape more" or "collect images".

Manages the flow of scraped fuel sign images from data/ingest/ into the labeling pipeline at data/tmp/. Handles dedup (by filename and content hash), manifest registration, and ingest cleanup. Use this skill whenever the user mentions "process ingest", "check ingest", "new images", "move scraped images", "run pipeline", "ingest to tmp", or asks about images waiting to be processed. Also trigger when the user wants to know what's in data/ingest/, or after a scraping session when images need to be moved into the labeling queue.

Stage 1 Labeler: Annotates sign_board bounding boxes on full camera photos for Finder training. This is the FIRST stage of a 2-stage labeling pipeline. Stage 1 (this skill): Find the sign in the photo → 1 bbox per image Stage 2 (sign-crop-labeler): Read the cropped sign → fuel rows, prices, brand Use this skill when the user wants to label images for the Finder model.

ML experiment runner following the scientific method. Handles hypothesis formation, dataset building, training, evaluation on canonical test sets, cross-experiment comparison, and experiment documentation in docs/experiments/EXP-NNN format. Use when the user says "train", "retrain", "experiment", "EXP-", "mAP", or wants to evaluate model performance.

Plans and executes scrape campaigns for Australian fuel station price sign images. Assesses current dataset state, brand gaps, and historical yield before recommending queries. Use this skill when the user wants to "scrape more", "collect images", "fill brand gaps", or "get more training data".

Stage 2 Labeler: Annotates fuel rows, prices, and brand within cropped sign images. This is the SECOND stage of a 2-stage labeling pipeline. Input: Cropped sign images produced by the Stage 1 Finder model. Output: Fuel row bounding boxes, price text, fuel types, brand classification. Use this skill ONLY after the Finder model reaches mAP50 > 0.70. DO NOT use this on full camera photos — use fuel-sign-labeler (Stage 1) instead.

Creates a full dated backup of all project assets NOT checked into git. Captures annotations, labels, images, previews, manifests, configs, experiment docs, scrape reports, model weights, and skills — everything needed to restore the full project state and continue work. Use when the user says "snapshot", "backup", "archive", or "save state".