machine-learning
5
activation-patching
Causal intervention via activation patching to identify important model components. Use when determining which layers, heads, or positions are causally responsible for model behavior.
ndif-team
data-ai
sorting
stars
current ordering strategy
query
all entries
refine the visible subset
machine-learning
5
fuzzy-augmentation-reject-inference
Build a Through-the-Door training set with reject inference using fuzzy augmentation, including PD-based sample weights; pairs with autogluon-tabularpredictor-fit for modeling the augmented data.
crossxwill
data-ai
machine-learning
5
monotonic-constraints-using-dictionaries
Define and apply monotonic constraints in AutoGluon using a constraints dictionary and a feature-ordered list for boosting models; depends on autogluon-tabularpredictor-fit for passing hyperparameters.
crossxwill
data-ai
machine-learning
5
custom-sklearn-estimator
Build scikit-learn compatible custom estimators by following the official “rolling your own estimator” rules for __init__, fit/predict, validation, learned attributes, tags, and estimator checks; prerequisite for autogluon-sklearn-wrapper or any sklearn-facing wrappers.
crossxwill
data-ai
machine-learning
5
model-steering
Control model behavior through persistent edits and steering interventions. Use when modifying model outputs, applying steering vectors, or creating persistently modified model versions.
ndif-team
data-ai
machine-learning
5
autogluon-sklearn-wrapper
Build a scikit-learn compatible wrapper for AutoGluon TabularPredictor with feature name checks, sample_weight support, and predict/predict_proba methods; depends on custom-sklearn-estimator for sklearn API rules and autogluon-tabularpredictor-class/fit for predictor usage.
crossxwill
data-ai
machine-learning
4
model-evaluation
Evaluates machine learning models for performance, fairness, and reliability using appropriate metrics and validation techniques. Trigger keywords: model evaluation, metrics, accuracy, precision, recall, F1, ROC, AUC, cross-validation, ML testing.
cosmix
data-ai
machine-learning
4
funsloth-runpod
Training manager for RunPod GPU instances - configure pods, launch training, monitor progress, retrieve checkpoints
chrisvoncsefalvay
data-ai
machine-learning
4
funsloth-check
Validate datasets for Unsloth fine-tuning. Use when the user wants to check a dataset, analyze tokens, calculate Chinchilla optimality, or prepare data for training.
chrisvoncsefalvay
data-ai
machine-learning
4
funsloth-train
Generate Unsloth training notebooks and scripts. Use when the user wants to create a training notebook, configure fine-tuning parameters, or set up SFT/DPO/GRPO training.
chrisvoncsefalvay
data-ai
machine-learning
4
example-agent
Guidance for selecting appropriate AI model (sonnet vs haiku) based on task complexity, reasoning requirements, and performance needs. Use when implementing agents or justifying model selection.
wahidyankf
data-ai
machine-learning
4
funsloth-upload
Generate comprehensive model cards and upload fine-tuned models to Hugging Face Hub with professional documentation
chrisvoncsefalvay
data-ai
machine-learning
4
multi-model
Multi-model battle for iterative (recursive) refinement. Rotates models every iteration and has other models judge/critique. Use when user asks to "battle models", "compare models", "multi-LLM", or wants iterative refinement across multiple OpenAI-compatible / Ollama models.
lollipopkit
data-ai
machine-learning
4
dspy-categorical
DSPy compositional prompt optimization with categorical signatures, module chaining, and automated prompt tuning. Use when building declarative LLM programs with typed signatures, composing multi-step reasoning modules (ChainOfThought, ReAct, ProgramOfThought), optimizing prompts with MIPROv2/BootstrapFewShot, or creating modular AI pipelines that separate program logic from prompt engineering.
manutej
data-ai
machine-learning
4
funsloth-hfjobs
Training manager for Hugging Face Jobs - launch fine-tuning on HF cloud GPUs with optional WandB monitoring
chrisvoncsefalvay
data-ai
machine-learning
4
qwen-training-data-miner-prototype
Qwen Training Data Miner (Prototype)
Foundup
data-ai
machine-learning
4
polynomial-functors
Spivak-Niu polynomial functor implementations for learner composition and dynamical systems. Use when modeling learning systems as categorical structures, composing machine learning components with polynomial functors, implementing the categorical framework from Polynomial Functors - A Mathematical Theory of Interaction, or building compositional dynamical systems with lenses and charts.
manutej
data-ai
machine-learning
4
funsloth-local
Training manager for local GPU training - validate CUDA, manage GPU selection, monitor progress, handle checkpoints
chrisvoncsefalvay
data-ai
machine-learning
3
time-series
ARIMA, SARIMA, Prophet, trend analysis, seasonality detection, anomaly detection, and forecasting methods. Use for time-based predictions, demand forecasting, or temporal pattern analysis.
pluginagentmarketplace
data-ai
machine-learning
3
validating-ai-ethics-and-fairness
Validate AI/ML models and datasets for bias, fairness, and ethical concerns. Use when auditing AI systems for ethical compliance, fairness assessment, or bias detection. Trigger with phrases like "evaluate model fairness", "check for bias", or "validate AI ethics".
BbgnsurfTech
data-ai
machine-learning
3
computer-vision
Image processing, object detection, segmentation, and vision models. Use for image classification, object detection, or visual analysis tasks.
pluginagentmarketplace
data-ai