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agent-o-rama
Layer 4 Learning and Pattern Extraction for Cognitive Surrogate Systems
Skills IA tools machine learning
Découvrez les skills tools machine learning, comparez leurs dépôts et consultez leurs instructions.
Catégories: tools machine learning
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cognitive-surrogate
Layer 6 Barton Cognitive Surrogate - build, train, validate psychological models with >90% fidelity
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curiosity-driven
Schmidhuber's curiosity-driven learning: Intrinsic motivation via compression progress. Seek states that improve world model.
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discrete-backprop
Gradient-free optimization via discrete perturbations and trit-based learning
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levin-levity
Leonid Levin''s algorithmic complexity meets playful mutual ingression. Use for: BB(n) prediction markets, Kolmogorov complexity rewards, WEV extraction from proof inefficiencies, Nash equilibrium between explo...
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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 iterat...
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investigate
Deep analytical investigation mode using Bayesian reasoning, superforecasting techniques, and rigorous hypothesis testing. Combines methods from Kahneman, Tetlock, Peirce, and Feynman. Use when user invokes /in...
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microimpute
ML-based variable imputation for survey data - used in policyengine-us-data to fill missing values
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l0
L0 regularization for neural network sparsification and intelligent sampling - used in survey calibration
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ml-benchmark
ML 모델 벤치마크 및 평가 실행. "벤치마크", "모델 평가", "성능 테스트", "inference 속도" 요청 시 활성화됩니다.
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model-sync
ML 모델 파일 서버 간 동기화. "모델 동기화", "모델 배포", "rsync 모델", "서버로 전송" 요청 시 활성화됩니다.
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gemini-claude-loop
Dual-AI engineering loop orchestrating Claude Code (planning/implementation) and Gemini (validation/review). Use when (1) complex feature development requiring validation, (2) high-quality code with security/pe...
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mlops-deployment
Docker, Kubernetes, CI/CD, model monitoring, and cloud platforms. Use for deploying ML models to production, setting up pipelines, or infrastructure.
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deep-learning
Neural networks, CNNs, RNNs, Transformers with TensorFlow and PyTorch. Use for image classification, NLP, sequence modeling, or complex pattern recognition.
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nlp-processing
Text processing, sentiment analysis, LLMs, and NLP frameworks. Use for text classification, named entity recognition, or language models.
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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.
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machine-learning
Supervised/unsupervised learning, model selection, evaluation, and scikit-learn. Use for building classification, regression, or clustering models.
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computer-vision
Image processing, object detection, segmentation, and vision models. Use for image classification, object detection, or visual analysis tasks.
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model-optimization
Quantization, pruning, AutoML, hyperparameter tuning, and performance optimization. Use for improving model performance, reducing size, or automated ML.
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learn
learn from the current session
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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...
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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 f...
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agent-selecting-models
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.
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episode-complete
Complete and score a learning episode to extract patterns and update heuristics. Use when finalizing a task to enable pattern extraction and future learning.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.