Discover tools machine learning skills, compare source repositories and read the instructions for each workflow.
Build ML pipelines, experiment tracking, and model registries. Implements MLflow, Kubeflow, and automated retraining. Handles data versioning and reproducibility. Use PROACTIVELY for ML infrastructure, experime...
Optimizes prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts. Expert in prompt patterns and techniques.
Build persistent project knowledge using checkpoint/recall. Activates when fixing bugs, making decisions, or investigating past work. Creates automatic knowledge base through systematic checkpointing and semant...
Machine learning model training, evaluation, deployment, and MLOps workflows.
Python best practices with modern frameworks, AI/ML integration, and performance optimization for 2025
Intelligent learning system optimizer that analyzes session patterns, identifies improvement opportunities, and continuously enhances Alfred's performance through adaptive learning and knowledge optimization. U...
19-agent team structure, decision trees for agent selection, Haiku vs Sonnet model selection, and agent collaboration principles. Use when deciding which sub-agent to invoke, understanding team responsibilities...
Practical workflows, context engineering strategies, and real-world execution examples for MoAI-ADK. Use when learning workflow patterns, optimizing context management, debugging issues, or implementing feature...
Perform comprehensive data analysis, statistical modeling, and data visualization by writing and executing self-contained Python scripts. Use when you need to analyze datasets, perform statistical tests, create...
Apply Chiral Narrative Synthesis (CNS) framework for contradiction detection and multi-source analysis using Tinker API for model training. Use when implementing CNS with Tinker for fine-tuning models on contra...
Vertieft den Marker-Engine-Skill um SFT/RL-Feinabstimmung mit LeanDeep 4.0; lädt Marker aus Supabase/ZIP und lernt eine Policy zur präzisen, kontextualisierten Marker-Anwendung bei strikter Bottom-up-Logik.
Adaptive learning system that tracks implementation patterns, success rates, and performance metrics to suggest proven solutions for future tasks.
Automatically score growth experiments using the ICE framework (Impact × Confidence × Ease). Use when the user creates a new experiment, mentions scoring or prioritization, or when analyzing experiment backlogs...
Orchestrate workflows across multiple AI models (Perplexity, GPT, Grok, Claude, Gemini) for comprehensive security research, competition execution, and strategic analysis using GUI interfaces or API automation
Multi-agent orchestration for comprehensive frontend + backend improvements. Use when user requests "improve the app", "optimize functionality", "make it perfect", or "enhance the system". Deploys specialized a...
ai-integration for learning technology evaluation and implementation.
Analyzes learner behavior, progress, and outcomes to generate actionable insights.
Expert in Apache Ray distributed computing. Use when converting Python code to Ray workloads, debugging Ray applications, optimizing Ray performance, or working with Ray Core, Ray Data, Ray Train, Ray Serve, or...
Optimize RAG performance with reranking, caching, parallel processing, and production deployment patterns. Use when improving retrieval quality, adding rerankers, deploying to production, implementing caching s...
Use when managing Shannon CLI performance and costs - check cache statistics, clear stale entries, set budgets, understand automatic model selection and cost optimization
Automatically applies when choosing LLM models and providers. Ensures proper model comparison, provider selection, cost optimization, fallback patterns, and multi-model strategies.
Automatically applies when evaluating LLM performance. Ensures proper eval datasets, metrics computation, A/B testing, LLM-as-judge patterns, and experiment tracking.
FEP Octave M3: 理論モジュール (I-E-S)。因果モデルを構築・更新し、世界観を形成する。 Use when: 「なぜ？」質問時、予測と結果が乖離した時、パターン検出が必要な時、根本原因分析時。 Use when NOT: 単純な事実確認のみ...
Run ML/deep learning experiments on Modal with robust error handling, W&B logging, and thorough documentation. Use when asked to: (1) Run an experiment, (2) Implement experiment handlers, (3) Fix and re-run fai...
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.