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| # /// script | |
| # dependencies = [ | |
| # "rp", | |
| # "nflx-mml-common-fs", | |
| # "nflx-mml-common-types", | |
| # "nflx-mml-common-io", | |
| # ] | |
| # /// | |
| import rp |
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| import rp | |
| stagger = 5 | |
| session_name = "Workbench" | |
| workbench_command = 'tmux a -d' | |
| query_url = "https://console.netflix.net/api/v1/workbenches" | |
| workbench_json = rp.curl_json(query_url, cookies="chrome") | |
| workbench_ids = [x["id"] for x in workbench_json["items"] if x["state"] == "running"] |
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| !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! | |
| !!! CRITICAL: DUMP FOLDER ISOLATION (NEVER BREAK THIS) !!! | |
| !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! | |
| Dumps live in `~/CleanCode/Dumps/` — each subfolder is a self-contained, portable project. | |
| If isolation breaks, the dump becomes non-portable and everything falls apart. | |
| **At the start of every conversation, check if your working directory is inside | |
| `~/CleanCode/Dumps/`. If so, you are in a dump and these rules apply.** |
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| import rp | |
| # https://gist.github.com/SqrtRyan/c33e4e40ccf74714a20c229a13c717fe | |
| from PIL import Image | |
| import rp | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import torchvision.transforms as T | |
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| """ | |
| Nanobanana edit quality filter — minimal, self-contained. | |
| pip install numpy imagehash pillow | |
| >>> from filter_sample import filter_pair | |
| >>> filter_pair('before.png', 'after.png') | |
| True # True = keep (good edit), False = reject (bad edit) | |
| Derived from .claude_auto_score/best_detector.py. [ <--- useful for Ryan] |
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| """ | |
| Nanobanana edit quality filter — minimal, self-contained. | |
| pip install numpy imagehash rp | |
| >>> from filter_sample import filter_pair | |
| >>> filter_pair('before.png', 'after.png') | |
| True # True = keep (good edit), False = reject (bad edit) | |
| Derived from .claude_auto_score/best_detector.py. |
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| """ | |
| Nanobanana edit quality filter — minimal, self-contained. | |
| pip install numpy imagehash rp | |
| >>> from filter_sample import filter_pair | |
| >>> filter_pair('before.png', 'after.png') | |
| True # True = keep (good edit), False = reject (bad edit) | |
| Derived from .claude_auto_score/best_detector.py. |
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| """ | |
| Nanobanana edit quality filter — minimal, self-contained. | |
| pip install numpy imagehash rp | |
| >>> from filter_sample import filter_pair | |
| >>> filter_pair('before.png', 'after.png') | |
| True # True = keep (good edit), False = reject (bad edit) | |
| Derived from .claude_auto_score/best_detector.py. |
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| """ | |
| Nanobanana edit quality filter — minimal, self-contained. | |
| pip install numpy imagehash rp | |
| >>> from filter_sample import filter_pair | |
| >>> filter_pair('before.png', 'after.png') | |
| True # True = keep (good edit), False = reject (bad edit) | |
| Derived from .claude_auto_score/best_detector.py. |
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| """ | |
| Nanobanana Edit Quality Detector — single-file, 4 rules, 90% recall. | |
| This is a self-contained copy of .claude_auto_score/best_detector.py. | |
| See .claude_auto_score/ for the full analysis pipeline, manifest, and report. | |
| Self-contained detector for video edit quality. No external dependencies | |
| beyond standard pip packages. Takes a sample folder (before.png + after.png), | |
| returns a JSON verdict. |
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