topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
CV-LB Gap Acknowledgment: CV improvement does NOT equal LB improvement. This is one of the most important MLOps principles. Use when: (1) OOF score keeps improving but LB score plateaus or drops, (2) Spending days tuning hyperparameters without LB improvement, (3) Comparing models on CV only without LB validation, (4)…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Avoid applying domain knowledge constraints that hurt ML model performance. Use when: (1) Considering medical/physical/logical constraints on training data, (2) Adversarial validation AUC changes significantly after applying constraints (>0.10 shift), (3) CV score drops after adding "reasonable" domain rules, (4)…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Diagnose when ensemble/stacking will fail due to high model correlation. Use when: (1) Stacking or ensemble shows no improvement over single models, (2) Considering weighted average of multiple models, (3) Model predictions are nearly identical, (4) AUC/RMS gains plateau despite complex ensembling. Critical for…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when you have run 3+ feature engineering experiments with no improvement, or when distance to theoretical upper bound is <15pp. Triggers when you suspect "I should stop optimizing features and try something else" — the most common failure mode is wasting weeks past the saturation point.
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when: (1) Considering switching from CPU to GPU for a Kaggle competition, (2) GPU quota is running low and you need to prioritize which competitions deserve GPU time, (3) About to start a model training run and unsure if GPU is worth the 30h/week quota cost, (4) Deciding between CPU AutoGluon vs GPU neural…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Apply GSD Core's loop-engineering methodology to ML/data-science tasks. Use when: (1) You have a multi-step ML pipeline (data → features → train → verify → submit), (2) Context is growing long and quality is drifting, (3) You want auditable verification at each step, (4) You want fresh-context subagents to handle…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when: (1) You have limited daily submission quota and 50+ skill files to explore, (2) Most Kaggle experiments cost 1 quota each but only the 1-2 BEST actually matter for your final score, (3) You waste hours doing redundant experiments because the EXISTING public kernels (rank 4-9 players) have already solved 80%…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
A guide for running Kaggle competitions, an online platform where people build models against shared datasets, and organizing research in a searchable NotebookLM knowledge base.
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when: (1) Entering a new Kaggle competition and unsure what type it is, (2) Need to choose between forking public kernels vs building custom, (3) Deciding how to allocate submission quota across the competition lifecycle, (4) Determining which evaluation/validation strategy to use. Covers 6 competition types…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Prevent wasted research by verifying Kaggle competition data format BEFORE investing in RAG, technical planning, or model architecture design. Use when: (1) Starting any new Kaggle competition, (2) Competition name/size is ambiguous about data format, (3) Planning to do extensive research before implementation, (4)…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when looking for top-solution tricks on a Kaggle competition, when needing CV-LB gap discussions, when validating a strategy against community experience, or when seeking hyperparameter advice from competition winners. Triggers on phrases like "what did top scorers do", "CV-LB gap in this competition", "winning…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Standard Operating Procedure for ANY Kaggle experiment or competition entry. Execute sequentially from Phase 0 to Phase 3. Each phase has explicit checklist items with pass/fail criteria. Covers: memory recall, competition type identification, public kernel evaluation, baseline establishment, controlled iteration…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when: (1) Your OOF score improved but LB didn't (or got worse), (2) You need to decide whether to submit based on OOF alone, (3) You're comparing models for ensemble inclusion and unsure which OOF signal to trust, (4) You see OOF/LB gap larger than 1% and don't know if it's overfitting or distribution shift.…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Optimal blending strategy for Kaggle competitions: 80/20 rule (re-ranking vs raw). Use when: (1) Re-ranking gives unexpected results, (2) Simple average blending underperforms, (3) Single model dominates ensemble. Avoids the trap of 100% re-ranking or equal-weight ensembles. UPDATED 2026-06-14: Added…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Match the submission file format to the competition's evaluation metric BEFORE submitting. For ranking-based metrics (AUC, logloss, MAP, NDCG, RMSLE) you MUST submit continuous probability/score values, not 0/1 class labels or rounded integers. Use when: (1) Preparing the final submission for any Kaggle competition…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Systematically replicate and learn from top Kaggle performers. Use when: (1) Stuck at a plateau and need new ideas, (2) Want to understand how top performers achieved their scores, (3) Looking for proven techniques from successful submissions, (4) OOF keeps improving but LB stays same or gets worse. Covers: code…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when: (1) You've completed a competition or experiment and want to extract reusable knowledge, (2) Your agent keeps repeating the same mistakes across competitions because lessons weren't crystallized, (3) You have 50+ memory files but can't find relevant knowledge when starting a new task, (4) You need to decide…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when: (1) Your agent's LB score drops dramatically week-over-week without code changes, (2) You're tempted to add "meta-aware" bonuses (priority targets, matchup bonuses, deck-type detection) to a strong heuristic, (3) Testing agent variants on small samples (60-80 games) and getting noisy/conflicting results, (4)…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when: (1) You have a strong hand-crafted baseline (heuristic, rule-based agent, domain-expert system) and want to improve it with ML, (2) Naive augmentation (search, behavioral cloning, type-classification) is making things WORSE, not better, (3) You're deciding between "more training data" vs "a learned value…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when: (1) Entering an LLM-based Kaggle competition (open-source model required, prompt engineering, few-shot), (2) Need to choose between base models (Qwen, DeepSeek, LLaMA), (3) Designing a prompt strategy (zero-shot, few-shot, chain-of-thought), (4) Deciding whether fine-tuning is worth the GPU cost vs prompt…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when exploring hyperparameter combinations for ML models, when doing Top-1% push on Kaggle, when grid search is too expensive, or when needing systematic exploration of pipeline variants. Triggers for neural architecture search, ensemble weight tuning, or feature subset selection.
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when extracting entities and relationships from text, when building dynamic knowledge graphs, when wanting to upgrade from static OKF to entity-aware memory, or when analyzing papers/discussions for concepts. Triggers when ingesting new papers, processing Kaggle writeups, or when existing skills can't capture…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when context window is getting crowded, when agent needs to recall past experiences, or when starting a long-running task that spans multiple sessions. Triggers when you have 43+ skills and can't fit them all in context, when you need to decide "what should be in working memory right now", or when persisting new…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
ML optimization sweet spot principle: "More is not always better". Use when: (1) Increasing model complexity but validation score plateaus or drops, (2) OOF keeps improving but LB stays same or gets worse, (3) Debating between simpler vs more complex models, (4) Feature selection or hyperparameter tuning, (5)…