GitHub
fine-tuning-expert
Use when fine-tuning LLMs, training custom models, or optimizing model performance for specific tasks. Invoke for parameter-efficient methods, dataset preparation, or model adaptation.
☆ 312View skill
tools machine learning AI Skills
Discover tools machine learning skills, compare source repositories and read the instructions for each workflow.
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GitHub
ml-pipeline
Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, or managing experiment tracking systems.
☆ 312View skill
GitHub
first-order-model-fitting
Fit first-order dynamic models to experimental step response data and extract K (gain) and tau (time constant) parameters.
☆ 283View skill
GitHub
mhc-algorithm
Implement mHC (Manifold-Constrained Hyper-Connections) for stabilizing deep network training. Use when implementing residual connection improvements with doubly stochastic matrices via Sinkhorn-Knopp algorithm....
☆ 283View skill
GitHub
jax-skills
High-performance numerical computing and machine learning workflows using JAX. Supports array operations, automatic differentiation, JIT compilation, RNN-style scans, map/reduce operations, and gradient computa...
☆ 283View skill
GitHub
validation-scripts
Data validation and pipeline testing utilities for ML training projects. Validates datasets, model checkpoints, training pipelines, and dependencies. Use when validating training data, checking model outputs, t...
☆ 283View skill
GitHub
scipy-curve-fit
Use scipy.optimize.curve_fit for nonlinear least squares parameter estimation from experimental data.
☆ 283View skill
GitHub
ml-model-training
Build and train machine learning models using scikit-learn, PyTorch, and TensorFlow for classification, regression, and clustering tasks
☆ 283View skill
GitHub
modal-gpu
Run Python code on cloud GPUs using Modal serverless platform. Use when you need A100/T4/A10G GPU access for training ML models. Covers Modal app setup, GPU selection, data downloading inside functions, and res...
☆ 283View skill
GitHub
nanogpt-training
Train GPT-2 scale models (~124M parameters) efficiently on a single GPU. Covers GPT-124M architecture, tokenized dataset loading (e.g., HuggingFace Hub shards), modern optimizers (Muon, AdamW), mixed precision...
☆ 283View skill
GitHub
machine-learning-ops-ml-pipeline
Design and implement a complete ML pipeline for: $ARGUMENTS
☆ 279View skill
GitHub
ml-engineer
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference...
☆ 279View skill
GitHub
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 temp...
☆ 279View skill
GitHub
llm-evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing...
☆ 279View skill
GitHub
mlops-engineer
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use P...
☆ 279View skill
GitHub
prompt-engineer
Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use when building AI...
☆ 279View skill
GitHub
agent-orchestration-improve-agent
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
☆ 279View skill
GitHub
ml-pipeline-workflow
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training a...
☆ 279View skill
GitHub
data-scientist
Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks,...
☆ 279View skill
GitHub
data-engineering-data-driven-feature
Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.
☆ 279View skill
GitHub
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
☆ 279View skill
GitHub
agentdb-learning-plugins
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, impleme...
☆ 272View skill
GitHub
reasoningbank-with-agentdb
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learnin...
☆ 272View skill
GitHub
reasoningbank-intelligence
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cogn...
☆ 272View skill
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