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bias-assessment
Evaluate AI systems for fairness using demographic parity, equalized odds, and bias detection techniques with mitigation strategies.
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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Explorer
GitHub
model-selection
Select appropriate AI/ML models based on capability matching, benchmarks, cost-performance tradeoffs, and deployment constraints.
GitHub
explainability-planning
Plan explainable AI (XAI) requirements including SHAP, LIME, attention visualization, and regulatory explainability needs.
GitHub
token-budgeting
Estimate and optimize AI/ML costs including token usage, context window management, batch processing, and caching strategies.
GitHub
hitl-design
Design human-in-the-loop workflows including review queues, escalation patterns, feedback loops, and quality assurance for AI systems.
GitHub
kaggle-api-expert
Expert agent for Kaggle API authentication, dataset management, and running Kaggle notebooks on Texas Tech HPCC. Specializes in connecting Jupyter notebooks to Kaggle API and submitting to code competitions. Al...
GitHub
flow-nexus-neural
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
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...
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...
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...
GitHub
behavioral-modes
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
GitHub
axolotl-config-management
Create, validate, and debug YAML training configurations for axolotl-rs fine-tuning
GitHub
describe-image
Uses a local model to describe something about an image
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...
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.
GitHub
gcp-deploy
Skill for deploying and managing agents on Google Cloud Platform, specifically Vertex AI Reasoning Engine.
GitHub
training-apo-agents
Train LangChain/LangGraph agents using Microsoft Agent-Lightning APO (Automatic Prompt Optimization). Use when user mentions APO training, prompt optimization, agent-lightning, training multi-agent systems, tra...
GitHub
ai-integration
Guide AI feature development using @monsoft/ai package with Langfuse telemetry. Use when building AI-powered features, text generation, or structured outputs.
GitHub
gokart
M3社が開発したPythonの機械学習パイプラインツールgokartに関する深い知識とベストプラクティスを提供する。Use when reviewing gokart task designs, implementing type-safe ML pipelines, writing gokart tests, or workin...
GitHub
agent-os-modelador-spec
Agent OS skill: modelador-spec
GitHub
openrouter
Use this skill when the user wants to call different LLM models through OpenRouter's unified API, compare model responses, track costs and response times, or find the best model for a task. Triggers include req...
GitHub
jax
Essential tools for using JAX in machine learning and mathematical analysis, covering core concepts, transformations, ML specifics, control flow, and parallelism.
GitHub
developing-flax-models
A comprehensive guide for developing, training, and managing neural networks using Flax NNX. Use when defining models, managing state, or writing training loops.
GitHub
training-mlps
A skill for defining and training Multi-Layer Perceptrons (MLPs) using Flax NNX.
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.