topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Avoid ensembling models with significantly different performance. Use when: (1) Considering weighted average of models with divergent OOF/LB scores, (2) One model clearly underperforms another, (3) Ensemble shows lower validation score than best individual model. Covers the negative weight effect where adding a weaker…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when designing a multi-agent system, when a single agent has too many responsibilities, when planning complex ML pipelines, or when observing "agent attention fragmentation" symptoms. Triggers when a task involves planning + coding + review steps.
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when: (1) considering adding family-level or store-level aggregation features to day-specific models, (2) tempted to copy hierarchical features from 1st place solutions, (3) CV improves but LB degrades after adding aggregated features. WARN: Multi-level aggregation features that work in unified models may OVERFIT…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use Google's Open Knowledge Format (OKF) to convert any directory of markdown files into a force-directed knowledge graph. Validated on docs/ml-agent-memory/: 12 concepts / 35 edges rendered in 5 seconds. Use when: (1) You have a directory of markdown notes with cross-references, (2) You want to visualize how your…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when: (1) Competing in ONNX-based competitions (NeuroGolf, model compression), (2) Need to design minimal neural networks as ONNX graphs, (3) Want to replace verbose agent-generated circuits with compact hand-designed nets. Key principles: single-node graphs have zero memory cost (output is free), Conv is a linear…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Per-category/per-family models can produce WORSE leaderboard scores than a single global model, even when per-category CV is better. Use when: (1) Considering training separate models per product category/family/store in tabular competitions, (2) Per-category CV improves but LB degrades, (3) Each category has <100K…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Progressive verification methodology for debugging complex systems. Use when: (1) Facing mysterious crashes or errors with no clear cause, (2) System works in some environments but not others, (3) Multiple potential failure points exist, (4) Need to isolate whether issue is environment configuration vs code bug. Core…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Augment a BC Transformer agent with 2-ply bounded search using the cg library's SearchBegin/SearchStep API. Validated 2026-07-30 on Pokemon TCG AI Battle: search-enhanced agent submitted as v8 (LB pending). Use when: (1) you have a trained BC policy/value model, (2) the cg library exposes a search API…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Submit a BC (behavioral cloning) Transformer agent for the Pokémon TCG AI Battle competition. Validated 2026-07-30: best.pth (15-epoch BC Large, dmodel=256) → public LB 600.0 (vs prior 165.6 — 3.6× improvement from using the best checkpoint instead of a stale submission). Use when: (1) training a BC agent on Kaggle…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Comprehensive EDA approach for retail/fashion/tabular ML using best-in-class libraries. Use during stage 1 (data understanding) of any ML pipeline. Built around 5-stage pipeline: (1) data quality with ydata-profiling + missingno, (2) statistical profiling with sweetviz, (3) domain-specific (RFM, transaction patterns…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when: (1) optimizing post-processing thresholds for RMSLE-evaluated competitions, (2) considering zeroing out small predictions, (3) implementing min-sales or adaptive thresholds for time series forecasting, (4) CV improves but LB degrades after changing post-processing, (5) comparing "smart" vs "simple" zeroing…
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Use when running Python code via sandbox, when an experiment fails with a runtime error, when building self-correcting ML agents, or when debugging auto-generated code. Triggers on subprocess failures, ImportError, CUDA OOM, shape mismatch, or any "agent gets stuck on syntax error" pattern.
topprismdata/cultivating-ml-agent
Skill Claude CodeCodex
Self-Consistency with Tool-Integrated Reasoning (SC-TIR) for mathematical problem solving. Use when: (1) Building mathematical reasoning systems, (2) Competing in math competitions (AIMO, AIME, MATH), (3) Need precise calculation beyond text generation, (4) LLM outputs contain calculation errors. Covers: SC-TIR…