machine-learning
6
machine-learning-ops-ml-pipeline
Design and implement a complete ML pipeline for: $ARGUMENTS
rmyndharis
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
5
feature-engineering-kit
Auto-generate features with encodings, scaling, polynomial features, and interaction terms for ML pipelines.
dkyazzentwatwa
data-ai
machine-learning
5
autogluon-tabularpredictor-predict-proba
Use TabularPredictor.predict_proba with full argument guidance for classification probability outputs; depends on autogluon-tabularpredictor-fit and supports autogluon-tabularpredictor-calibrate-decision-threshold/set-decision-threshold.
crossxwill
data-ai
machine-learning
5
autogluon-tabularpredictor-fit
Detail every TabularPredictor.fit argument, including presets, ensembling, resources, HPO, and deployment settings; depends on autogluon-tabularpredictor-class and feeds autogluon-tabularpredictor-fit-summary, predict-proba, calibrate-decision-threshold, set-model-best, save/load.
crossxwill
data-ai
machine-learning
5
autogluon-tabularpredictor-set-decision-threshold
Set binary decision thresholds with TabularPredictor.set_decision_threshold and understand its single argument; depends on autogluon-tabularpredictor-calibrate-decision-threshold or autogluon-tabularpredictor-predict-proba for threshold selection.
crossxwill
data-ai
machine-learning
5
autogluon-tabular-indepth-tutorial
Consolidated AutoGluon Tabular tutorial summary (essentials + how-it-works + in-depth), explaining the workflow and every highlighted argument; depends on autogluon-tabularpredictor-class and autogluon-tabularpredictor-fit, plus autogluon-tabularpredictor-calibrate-decision-threshold/set-decision-threshold for threshold tuning.
crossxwill
data-ai
machine-learning
5
autogluon-tabularpredictor-calibrate-decision-threshold
Calibrate binary classification decision thresholds with TabularPredictor.calibrate_decision_threshold, detailing every argument and trade-off; depends on autogluon-tabularpredictor-fit and pairs with autogluon-tabularpredictor-set-decision-threshold and autogluon-tabularpredictor-predict-proba.
crossxwill
data-ai
machine-learning
5
autogluon-tabularpredictor-fit-summary
Generate and interpret training summaries with TabularPredictor.fit_summary, detailing all arguments and outputs; depends on autogluon-tabularpredictor-fit and complements autogluon-tabularpredictor-set-model-best.
crossxwill
data-ai
machine-learning
5
mlflow-evaluation
MLflow 3 GenAI evaluation for agent development. Use when (1) writing mlflow.genai.evaluate() code, (2) creating @scorer functions, (3) building evaluation datasets from traces, (4) using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), (5) analyzing traces for latency/errors/architecture, (6) optimizing agent context/prompts/token usage, (7) debugging evaluation failures. Covers the full eval workflow: trace analysis -> dataset building -> scorer creation -> evaluation execution.
databricks-solutions
data-ai
machine-learning
5
eval-coach
Guide users through building comprehensive AI evaluation strategies using Evaluation-Driven Development (EDD)
BayramAnnakov
data-ai
machine-learning
5
classification-helper
Quick classifier training with automatic model selection, hyperparameter tuning, and comprehensive evaluation metrics.
dkyazzentwatwa
data-ai
machine-learning
5
prompt-engineer
Comprehensive LLM prompt engineering and optimization workflow that orchestrates expert analysis, advanced techniques, and multi-domain optimization using the integrated toolset. Handles everything from basic prompt improvement to complex multi-agent prompt orchestration.
dy9759
data-ai
machine-learning
5
pi-orchestration
Orchestrate multiple AI models (GLM, MiniMax, etc.) as workers using Pi Coding Agent with Claude as coordinator.
clawdbot
data-ai
machine-learning
5
nnsight-basics
Core nnsight concepts for neural network interpretability. Use when setting up models, tracing activations, saving values, or making basic interventions on model internals.
ndif-team
data-ai
machine-learning
5
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
cndoit18
data-ai
machine-learning
5
linear-algebra-expert
Expert in vector spaces, matrices, linear transformations, eigenvalues, and applications to data science and machine learning
sandraschi
data-ai
machine-learning
5
autogluon-tabularpredictor-save
Save TabularPredictor artifacts with TabularPredictor.save, detailing all arguments and persistence behavior; depends on autogluon-tabularpredictor-fit and pairs with autogluon-tabularpredictor-load.
crossxwill
data-ai
machine-learning
5
ml-model-explainer
Explain ML model predictions using SHAP values, feature importance, and decision paths with visualizations.
dkyazzentwatwa
data-ai
machine-learning
5
autogluon-tabularpredictor-set-model-best
Set the default prediction model with TabularPredictor.set_model_best, detailing all arguments and effects; depends on autogluon-tabularpredictor-fit and is often guided by autogluon-tabularpredictor-fit-summary or leaderboard outputs.
crossxwill
data-ai
machine-learning
5
autogluon-tabularpredictor-load
Load saved predictors with TabularPredictor.load, detailing all arguments and security/version implications; depends on autogluon-tabularpredictor-save and assumes a prior autogluon-tabularpredictor-fit.
crossxwill
data-ai
machine-learning
5
logit-lens
Decode intermediate layer predictions using the Logit Lens technique. Use when analyzing what a model predicts at each layer, understanding information flow, or visualizing layer-wise processing.
ndif-team
data-ai
machine-learning
5
autogluon-tabularpredictor-class
Explain and configure AutoGluon’s TabularPredictor constructor, including all init arguments and their effects for tabular classification/regression; prerequisite for autogluon-tabularpredictor-fit, predict-proba, save/load, fit-summary, calibrate-decision-threshold, set-decision-threshold, and set-model-best.
crossxwill
data-ai
machine-learning
5
evaluator-optimizer
Evaluator-Optimizer pattern knowledge for automatic iteration cycles. Implements Anthropic's agent architecture pattern for continuous improvement. Triggers: evaluator-optimizer, iteration pattern, 평가-최적화, 評価最適化, 评估优化
popup-studio-ai
data-ai
machine-learning
5
model-comparison-tool
Use when asked to compare multiple ML models, perform cross-validation, evaluate metrics, or select the best model for a classification/regression task.
dkyazzentwatwa
data-ai