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Claude Skills by TranBaDat2607
github.com/TranBaDat260741 skills0 installs0 views
- GradersShould-not-trigger check: no kaggle-grandmaster skill is loaded for a request that is not Kaggle competition work. Scored in both arms, so a plugin that over-triggers shows a negative delta.Votes: 0GitHub stars: 2
- Audio And SignalPlaybook for Kaggle audio and biosignal competitions — bioacoustics (BirdCLEF-style), speech, EEG/ECG/EMG, seismic and other 1D signals — covering spectrogram vs raw-waveform models, augmentation, weak labels, soundscape inference under CPU limits, and subject-grouped validation. Use for any audio or sensor-waveform data.Votes: 0GitHub stars: 2
- Code CompetitionsShipping solutions to Kaggle code competitions — offline notebooks with internet disabled, packaging models/wheels/code as Kaggle datasets, hidden test set re-runs, runtime and memory budgets on T4x2/P100/L4/TPU/CPU, evaluation API patterns, and debugging submission errors (Scoring Error, timeout, OOM). Use when a competition requires notebook submission or when preparing an inference kernel.Votes: 0GitHub stars: 2
- Competition ReconHow to analyse a Kaggle competition before modelling — metric, data, rules, timeline, public notebooks, discussions, leaks, past similar competitions and shake-up risk. Use at the start of a competition, when joining late, or when the user asks to research/understand a competition.Votes: 0GitHub stars: 2
- Competition StrategyThe career layer of becoming a Kaggle Competitions Grandmaster — what the title requires (golds, a solo gold), which competitions award medals and which to enter, planning a solo gold, joining late, running several competitions at once, and teaming — when and with whom to merge, the private-sharing and submission-count rules around merging, and the technical merge protocol (shared folds, OOF exchange, joint final selection). Use when the user asks about teams, merging, choosing competitions, ...Votes: 0GitHub stars: 2
- Computer VisionWinning playbook for Kaggle computer vision competitions — image classification, object detection, segmentation, medical imaging (CT/MRI/X-ray/pathology), video, satellite — covering backbone choice (timm), resolution, augmentations, losses, training recipes, TTA, pseudo-labelling, multi-stage pipelines and inference under time limits. Use for any image/video data.Votes: 0GitHub stars: 2
- Cv Lb DebuggingSystematic diagnosis when cross-validation and leaderboard disagree, when a score is suspiciously good or bad, or when an improvement in CV does not transfer to LB — leakage hunting, distribution shift analysis, inference/training mismatch, metric bugs, and public LB noise estimation. Use when CV and LB diverge or a result looks wrong.Votes: 0GitHub stars: 2
- Deep Learning TrainingCompetition-grade deep learning training practice in PyTorch — schedules, optimisers, mixed precision, EMA/SWA, gradient accumulation and checkpointing, multi-GPU on Kaggle (2xT4), data loading speed, reproducibility, debugging loss curves, and making the most of limited GPU hours. Use when writing or debugging any neural network training loop for a competition.Votes: 0GitHub stars: 2
- EnsemblingHow Kaggle Grandmasters build ensembles — OOF management, diversity, averaging/rank/geometric blending, Caruana hill climbing, constrained weight optimisation, stacking with nested CV, multi-level and residual stacking over large model libraries, library pruning, multi-seed and multi-fold averaging, and avoiding blend overfitting. Use when combining models or when asked to squeeze the final points out of a leaderboard.Votes: 0GitHub stars: 2
- Final Submission SelectionChoosing the final Kaggle submissions to maximise expected private leaderboard score and survive shake-ups — CV vs public LB weighting, hedging strategies, robustness checks, and endgame timeline. Use in the last days of a competition or when the user asks which submissions to select.Votes: 0GitHub stars: 2
- Grandmaster PlaybookThe master operating procedure for competing in a Kaggle (or Kaggle-style) machine learning competition at Grandmaster level. Use whenever the user is working on a Kaggle competition, wants to improve a leaderboard score, asks how to approach a data science competition, or is in a workspace containing .kaggle-gm/ — it routes to the specialised skills (recon, validation, tabular, vision, NLP/LLM, time series, ensembling, code competitions, final selection).Votes: 0GitHub stars: 2
- Hyperparameter TuningEfficient hyperparameter optimisation for Kaggle — when tuning is worth it, Optuna recipes with pruning on frozen folds, search spaces for LightGBM/XGBoost/CatBoost/NNs/transformers, budget allocation, and avoiding CV overfitting from excessive search. Use when the user wants to tune models or asks for an Optuna setup.Votes: 0GitHub stars: 2
- Kaggle CliUsing the official Kaggle CLI/API from Claude Code — authentication, downloading competition data, submitting (and reading scores), listing submissions and leaderboards, pushing/pulling notebooks (kernels), creating and versioning datasets and models, and fetching episode replays. Use whenever interacting with kaggle.com programmatically.Votes: 0GitHub stars: 2
- Kaggle GpuTraining on Kaggle's free GPUs and TPUs under their weekly quotas — choosing T4x2, P100, TPU v5e-8 or a CPU kernel, remote training runs with kgkit gpu (build/push/wait/collect), one-fold screens with pruning, resuming past the session limit, porting to torch_xla, and budgeting accelerator hours until the deadline. Use when training on Kaggle notebooks/kernels, planning or rationing GPU/TPU quota, choosing an accelerator, or when a remote run was slow, idle, queued, timed out or wasted hours.Votes: 0GitHub stars: 2
- Kg AdoptAdopt an existing mid-competition Kaggle repo in place — write .kaggle-gm/ (competition facts), import the past Kaggle submissions and the experiments the repo already tracks, mark the accepted baseline — so the hooks, budget checks and kgkit work. Use instead of kg-start when the competition work already exists (kernel folders, an experiment log, a validation set).Votes: 0GitHub stars: 2
- Kg BaselineBuild fast, strong, correctly-validated baselines for the current competition from the plugin's templates — the main model plus diverse families on the same folds — log them to the ledger, decide the accepted baseline and produce a validated submission file.Votes: 0GitHub stars: 2
- Kg CvDesign, build, verify and freeze the cross-validation scheme (data/folds.csv) so it mirrors how the test set was split.Votes: 0GitHub stars: 2
- Kg DebugDiagnose a CV-LB disagreement, a suspicious score jump, or a failed/odd submission using the systematic CV-LB debugging procedure and the validation auditor.Votes: 0GitHub stars: 2
- Kg EdaCompetition-focused EDA — red flags, leaks, drift, hidden groups, adversarial validation — written to reports/eda.md.Votes: 0GitHub stars: 2
- Kg EnsembleBuild the best honest ensemble from ledger experiments (diversity analysis, hill climbing / weights / rank / stacking with nested CV, post-processing re-tune) and write a validated blended submission.Votes: 0GitHub stars: 2
- Kg ExperimentRun one disciplined experiment for a hypothesis — single change, frozen folds, same seeds, ledger logging with its parent baseline, paired comparison (per-fold deltas + OOF bootstrap) against the accepted baseline, recorded keep/discard decision.Votes: 0GitHub stars: 2
- Kg FinalChoose the final Kaggle submissions to maximise expected private leaderboard score — evidence table, LB noise estimate, CV-vs-LB weighting, hedge selection and robustness checks.Votes: 0GitHub stars: 2
- Kg GpuRun training on Kaggle's GPUs within the weekly quota — plan the GPU budget, screen ideas on one fold (two per 2xT4 session, with pruning), promote winners to full-fold remote runs, resume timed-out runs, collect results into the ledger with GPU-hour accounting.Votes: 0GitHub stars: 2
- Kg GrindAutonomous improvement loop — repeatedly pick the highest-value idea from the backlog, run it as a disciplined experiment against the accepted baseline, decide with a paired comparison, update the backlog, and re-check the CV for overfitting, within a time or experiment budget.Votes: 0GitHub stars: 2
- Kg IdeasGenerate a ranked, evidence-backed backlog of next experiments from error analysis, the domain playbook's untried levers, public notebooks, discussion/prior-art research and the ledger history, stored with kgkit backlog.Votes: 0GitHub stars: 2
- Kg KernelPackage the current best solution for a Kaggle code competition — offline inference script, weight/code/wheel datasets, kernel metadata with internet off, runtime estimate on hidden-test size, push and poll.Votes: 0GitHub stars: 2
- Kg PostmortemAfter a competition ends — record every public and private score, check what the finals scored, measure which signals predicted the private leaderboard, study the top solutions, and distil reusable lessons into a post-mortem and a cross-competition lessons file.Votes: 0GitHub stars: 2
- Kg ReconResearch a Kaggle competition (metric, data, rules, timeline, top notebooks, discussions, similar past competitions, leak and shake-up risk) and write reports/recon.md.Votes: 0GitHub stars: 2
- Kg ShowcaseAfter a competition ends — turn the work into a public repository with two ways in. solution/ for readers who want the result (the final pipeline and the exact commands), journey/ for readers who want to learn from it (how the ideas developed, phase by phase, written from the ledger, with links to the code each experiment ran). Tidies the tree, rebuilds the README, adds a license and figures, audits a public notebook and weights, drafts a Kaggle write-up and tags the final state. Asks before ...Votes: 0GitHub stars: 2
- Kg StartBootstrap a Kaggle competition workspace end to end — recon, data download, workspace init, EDA, adversarial validation, frozen folds, metric implementation, baseline model and a first validated submission file.Votes: 0GitHub stars: 2
- Kg StatusShow where the competition stands — metric, deadline, accepted baseline, best CV, best LB, CV-LB correlation, recent experiments and decisions, top of the backlog, submissions left today — and recommend the next actions.Votes: 0GitHub stars: 2
- Kg SubmitValidate a submission file, check the daily budget, submit it to Kaggle with a ledger-linked message, poll for the public score and record it in the ledger.Votes: 0GitHub stars: 2
- Leaderboard BoostersAdvanced techniques that move a solid solution into the medal zone — pseudo-labelling, knowledge distillation, test-time augmentation, external data and pretraining, data cleaning/relabelling, multi-stage pipelines, auxiliary targets, leak exploitation, and full-data retraining. Use when a strong baseline exists and the user wants extra leaderboard gains.Votes: 0GitHub stars: 2
- Metric OptimizationOptimising directly for the Kaggle evaluation metric — choosing training losses that align with AUC, logloss, RMSE/RMSLE/MAE, F1/F-beta, QWK, MAP@K, IoU/Dice, mAP, pinball and custom metrics; threshold and rounding optimisation, calibration, metric-aware post-processing and exact metric re-implementation. Use whenever deciding a loss, post-processing, or when a metric seems hard to optimise.Votes: 0GitHub stars: 2
- Nlp And LlmWinning playbook for Kaggle NLP and LLM competitions — transformer fine-tuning (DeBERTa-v3 and friends), LLM fine-tuning with LoRA/QLoRA, LLM-as-solver (math/reasoning/agents), retrieval & RAG, synthetic data generation, distillation, efficient inference with vLLM under Kaggle time limits, and text-specific validation. Use for any text, chat, code, or LLM-related competition.Votes: 0GitHub stars: 2
- Recsys And RankingPlaybook for Kaggle recommendation and ranking competitions (OTTO, H&M, session-based recommendation, search relevance, learning-to-rank) — candidate generation (co-visitation, recency, popularity, embeddings), feature engineering for user-item pairs, LambdaRank/GBDT rankers, MAP@K/NDCG/recall validation, and memory-efficient pipelines. Use when predictions are ranked lists of items per user/query/session.Votes: 0GitHub stars: 2
- Simulation And OptimizationPlaybook for Kaggle simulation competitions (agents playing games in an environment against other teams' bots, e.g. Lux AI, Kore, ConnectX, Halite) and combinatorial optimisation competitions (Santa / traveling-salesman / scheduling / packing puzzles). Use when the submission is an agent or a solution file scored by an objective rather than predictions on a test set.Votes: 0GitHub stars: 2
- Tabular MasteryWinning playbook for tabular Kaggle competitions (including Playground Series) — GBDT (LightGBM, XGBoost, CatBoost) recipes, feature engineering patterns, categorical handling, NN models for tabular (MLP, TabM, transformers), GPU acceleration, synthetic-data competitions, and tabular ensembling. Use for any rows-and-columns problem.Votes: 0GitHub stars: 2
- Time SeriesWinning playbook for Kaggle time-series and forecasting competitions — leak-free time validation, lag/rolling/calendar features, global GBDT models, recursive vs direct multi-horizon forecasting, hierarchical reconciliation, deep forecasting models, financial/market prediction with purged CV, and sensor/event sequences. Use whenever the target depends on time or the test period is in the future.Votes: 0GitHub stars: 2
- Validation StrategyDesigning a cross-validation scheme that tracks the private leaderboard — choosing KFold/Stratified/Group/StratifiedGroup/multilabel/time-series/purged splits, adversarial validation, CV-LB correlation, noise estimation, and leakage prevention. Use when setting up CV, when CV and LB disagree, or before trusting any score.Votes: 0GitHub stars: 2
- Compute EfficiencySpeed defaults for Kaggle training and inference by domain (images, text and LLMs, tabular with cuDF/cuML/GPU GBDTs, audio, recsys, time series) and hardware (T4x2, P100, TPU, CPU-only kernels, Windows): measuring idle, input-bound or compute-bound runs, mixed precision, data loading, torch.compile, ONNX/OpenVINO/TensorRT/vLLM and quantisation. Use whenever training or inference code is written or changed, and when a run is slow, a GPU idles or runs out of memory, or a kernel must fit a time ...Votes: 0GitHub stars: 2