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Combine candidate submissions, post-process, verify, and submit to Kaggle.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Use this skill for any task in the cayley IHES Picture Cube project — training, solving, evaluation, ensembling, submission. Covers the full ML+search pipeline.
Strat-5 acceptance gate for a megaminx V model. Runs 51-pid stratified eval at beam 65k, compares to m05 baseline (50/51 / mean 89.4), reports pass/fail.
Run the top-N long-pid sym-ensemble rescue + merge cycle on the current best megaminx submission.
One-shot status across GCP solves, Kaggle kernels, and local runs for the megaminx competition.
Verify a megaminx submission CSV, submit to Kaggle, and update HANDOFF / SUBMISSION_JOURNEY / to_do_shortlist with the new score.
Pull the latest cayleypy-tpu-beam-smoke kernel output, aggregate per-rank JSONs, merge with current best, verify, and prep for submission.
| name | source-command-submit |
| description | Combine candidate submissions, post-process, verify, and submit to Kaggle. |
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_$(date +%Y%m%d_%H%M%S).csv — this is the raw combine output before post-processing.
Run the combine step:
.venv/Scripts/python.exe scripts/combine_submissions.py \
--candidates <CSVs> \
--fallback data/kociemba_fallback.csv \
--out <raw_out>
Run the post-process step (appending _pp to the output name):
.venv/Scripts/python.exe scripts/post_process_submission.py \
--in <raw_out> --out <pp_out> \
--bfs-table data/bfs_table_d5.pkl
Report the combined total move count and post-processed total.
Ask the user to confirm submission (the -m description is whatever was passed to /submit). Run:
export KAGGLE_API_TOKEN=$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
SubmissionStatus.COMPLETE with score 999999 or similar on Kaggle (silent failure).data/kociemba_fallback.csv as the fallback floor; never use sample_fallback.csv (sample-quality, 500K+ moves).