Browse Skills

Comprehensive guide to designing AI-powered products covering UX patterns, trust and transparency, error handling, feedback loops, ethical considerations, and user research for AI features.

Designs end-to-end computer vision pipelines including model selection (CNN/ViT/YOLO), data augmentation, training strategies, transfer learning, and production deployment with ONNX/TensorRT optimization for cloud and edge.

Expert in AI cost optimization — token efficiency, model routing, prompt caching, GPU cost management, and inference optimization strategies for LLMs and ML systems.

Design and implement anomaly detection systems for tabular data, time series, and streaming environments. Guides algorithm selection, feature engineering, threshold tuning, and production deployment with focus on minimizing false positives.

Expert in feature store design and operations — real-time feature serving, offline/online consistency, feature pipelines, and ML platform integration with Feast, Tecton, and custom solutions.

Production-grade machine learning pipeline engineering with scikit-learn. Covers preprocessing pipelines, feature engineering, model selection, cross-validation, hyperparameter tuning, serialization, and deployment patterns for reliable ML systems.

Transforms content to channel-optimal lengths using empirical engagement data for 12+ platforms. Handles truncation-aware restructuring, hook positioning, and format-specific constraints from Reddit to TikTok.

Designs, builds, and validates high-quality training datasets for LoRA fine-tuning of large language models, applying data-centric AI principles and instruction-tuning best practices to maximize model performance per training example.

Transforms Claude into an evidence-based Socratic tutor that teaches through structured questioning rather than direct answers. Applies cognitive science frameworks (Bloom's taxonomy, zone of proximal development, cognitive load theory, retrieval practice) to guide learners toward genuine understanding. Adapts question complexity, manages scaffolding, and builds metacognitive skills across any subject domain.

Designs and optimizes vector embedding systems for semantic search, similarity matching, and retrieval applications. Covers embedding model selection, dimensionality reduction, vector database architecture, and search quality optimization.

Designs effective prompts and prompt chains for LLM applications. Covers chain-of-thought reasoning, few-shot learning, system prompt architecture, output formatting, prompt evaluation, and reusable template design.

Senior MLOps drift detection specialist that designs production-grade monitoring pipelines with statistical tests, alerting thresholds, and automated retraining triggers.

The concierge for 1,100+ SupaSkills. Describe what you need in plain language and Skill Navigator diagnoses your task, identifies the right domain and category, and recommends 1 to 3 specific skills with clear reasoning. Like a librarian who knows every book on every shelf.

Designs comprehensive data labeling strategies including annotation pipeline architecture, inter-annotator agreement measurement, active learning loops, label quality control, crowdsourcing management, few-shot labeling, and weak supervision techniques.

Expert guidance for selecting, fine-tuning, and deploying transformer-based NLP models using the Hugging Face ecosystem and PyTorch for production natural language processing tasks.

Guides practitioners through end-to-end LoRA and QLoRA fine-tuning workflows, from dataset curation and hyperparameter selection to training optimization, evaluation, model merging, and deployment of parameter-efficient fine-tuned language models.

Guides ML practitioners in designing rigorous experiment tracking workflows using MLflow, Weights & Biases, and related tools, covering hyperparameter optimization, reproducibility, model registry, and team collaboration patterns.

Expert guidance for maximizing LLM context window effectiveness through token budgeting, prompt compression, information placement, caching strategies, and cost-efficient context management.

Designs automated machine learning pipelines for model selection, hyperparameter optimization, and neural architecture search. Covers AutoML frameworks (Optuna, Ray Tune, Auto-sklearn), search strategies, and production integration.

Design production-grade hybrid search systems that combine BM25 keyword search with vector-based semantic retrieval, multi-stage reranking pipelines, and RAG retrieval layers for optimal relevance and recall.

Design AI agent systems with precisely calibrated human oversight, mapping every action to the right autonomy level — from fully automatic to synchronous approval — using established frameworks for trust calibration, escalation policies, and audit trail architecture.

Architects multimodal AI systems that combine vision, language, and audio modalities. Designs cross-modal fusion strategies, selects appropriate models (CLIP, GPT-4V, Gemini), and builds end-to-end multimodal pipelines.

Expert in TensorFlow and Keras — model building, training, TF Serving, TF Lite, TFX pipelines, and production deployment on Google Cloud.

Expert guidance on designing, building, and querying knowledge graphs using ontology engineering, graph databases, NLP-based construction pipelines, and LLM/GraphRAG integration patterns.