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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".
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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.
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".
| name | scrape-dispatch |
| description | 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". |
Never scrape blind. Before planning queries:
Run a Python script to count brands in data/tmp/annotations/*.json and manifest status.
Prioritize brands with the largest gaps to targets (50+ for majors, 25+ for medium, 15+ for minor). Gap = Target - Current count.
Check skip vs. label ratios in data/tmp/pipeline_events.jsonl.
RULE: ~60% proven winners + ~40% new experimental queries.
"APCO fuel price sign {City} Victoria""EG Foodary fuel price pylon {City} Australia""petrol price war Australia sign board cheap fuel"Template: "{Brand} fuel price sign {City/Suburb} {State} Australia"
Higher yield: Wodonga/Bendigo (APCO), Gregory Hills (EG), Bankstown (Metro), Rockhampton (Puma).
Template: "fuel price {brand} {city} site:{news_domain}"
(e.g., Canberra Times, Adelaide Now).
Target the biggest gaps using state+brand templates.
python scripts/scrape_google_images.py --query "{query}" --brand {brand} --max {count}
.venv/bin/python .claude/skills/data-pipeline/scripts/process_ingest.py
Once ingested to data/tmp/, proceed to fuel-sign-labeler.