Source command submit

Neural distance heuristics + GPU/TPU beam search for the CayleyPy IHES Picture Cube and Megaminx puzzles

Install

npx -y skills add Erlemar/cayley-puzzles --skill source-command-submit

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

Copied from the file, not written here

Combine candidate submissions, post-process, verify, and submit to Kaggle.

2.6 KB, as published. Nobody here has run it

Use this skill when the user asks to run the migrated source command submit.

Bundles the 4-command submission pipeline:

combine_submissions.py — take min-length per puzzle across all candidate CSVs + Kociemba fallback.
post_process_submission.py — pair-cancel + BFS-d5 window shortening.
verify_submission sanity check on the final CSV.
kaggle competitions submit.

/submit <description> — uses all submissions/*.csv as candidates EXCEPT the output of previous /submit runs (named sub_*.csv).
/submit --only a.csv b.csv ... "description" — specify candidates explicitly.

Do the following, in order:

Pick candidate CSVs. Unless the user passed --only, default to submissions/fast_b8k_mitm6.csv submissions/s1_b4k.csv submissions/s2_b4k.csv submissions/s3_b4k.csv submissions/e3_khoruzhii_b65k.csv submissions/e5_khoruzhii_b65k.csv (these are the landmark runs that have always helped in ensembles — verify they exist before using).
Generate a timestamped output path submissions/sub_$(date +%Y%m%d_%H%M%S).csv — this is the raw combine output before post-processing.

Run the combine step:

.venv/Scripts/python.exe scripts/combine_submissions.py \
  --candidates <CSVs> \
  --fallback data/kociemba_fallback.csv \
  --out <raw_out>

Run the post-process step (appending _pp to the output name):

.venv/Scripts/python.exe scripts/post_process_submission.py \
  --in <raw_out> --out <pp_out> \
  --bfs-table data/bfs_table_d5.pkl

Ask the user to confirm submission (the -m description is whatever was passed to /submit). Run:

export KAGGLE_API_TOKEN=$KAGGLE_API_TOKEN
.venv/Scripts/kaggle.exe competitions submit -c cayleypy-ihes-cube -f <pp_out> -m "<description>"

After submission, immediately check status with:

.venv/Scripts/kaggle.exe competitions submissions cayleypy-ihes-cube | head -3

Never skip step 2 verification — a path with an unknown generator name will return SubmissionStatus.COMPLETE with score 999999 or similar on Kaggle (silent failure).
If any candidate CSV doesn't exist, remove it from the list and proceed — don't abort the whole pipeline.
Always reuse the same data/kociemba_fallback.csv as the fallback floor; never use sample_fallback.csv (sample-quality, 500K+ moves).