Browse Skills

A meta-skill that makes AI coding assistants think strategically before acting. Implements a 5-factor risk heuristic (Reversibility, Blast Radius, Coupling, Stakes, Uncertainty) to determine whether a code change needs shallow execution, medium analysis, or deep pre-mortem reasoning. Based on cognitive science, game theory, and software engineering economics research.

Comprehensive prompt engineering guidance covering techniques (chain-of-thought, few-shot, system prompts), evaluation, guardrails, structured output, and building reliable AI interactions.

Comprehensive guide to Claude Code CLI covering skills, MCP servers, hooks, slash commands, project setup, CLAUDE.md configuration, and workflows for maximizing AI-assisted development.

Systematically removes LLM fingerprints from text using evidence-based linguistic research. Replaces machine patterns (vocabulary, structure, tone) with human writing characteristics while preserving meaning and quality.

Master XGBoost for tabular data problems including hyperparameter optimization, feature engineering, SHAP-based interpretability, and production deployment with drift monitoring.

Design safe, effective tool-using AI agents with proper tool schemas, agent loop architecture, safety gates, and evaluation strategies for production LLM applications.

Design, structure, and optimize production-grade system prompts for large language models using proven architectural patterns, research-backed techniques, and adversarial testing methodologies.

Design reliable structured data extraction and generation systems powered by LLMs, with JSON Schema design, provider-specific configuration, validation pipelines, and production error handling.

Guides the design of reinforcement learning systems including policy selection (PPO, SAC, DQN), environment design, reward shaping, exploration strategies, multi-agent configurations, and sim-to-real transfer for robotics and game AI.

Helps you set up and optimize MLflow for experiment tracking, run comparison, model registry management, and production MLOps workflows across any ML framework.

Expert architect for designing multi-step prompt chains and orchestration patterns — from task decomposition and chain-of-thought sequencing to intermediate verification gates, context window management, error recovery strategies, and production-grade prompt pipeline design for complex LLM workflows.

Evaluates source credibility, detects fabricated citations, and assesses claim reliability using SIFT, CRAAP, and domain-specific evidence hierarchies. Essential for verifying LLM output and research findings.

Designs recommendation systems covering collaborative filtering, content-based filtering, hybrid approaches, cold start solutions, embedding models, real-time serving, and A/B testing strategies for personalized user experiences.

Comprehensive guide to integrating LLMs into applications covering API usage, RAG, agents, evaluation, cost optimization, streaming, and production deployment patterns.

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.

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.

Systematic research methodology for LLM-assisted workflows. Transforms vague questions into structured inquiry plans using query decomposition, multi-pass retrieval, evidence synthesis, and PRISMA-inspired reporting.

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.

Builds structured competitive intelligence analyses including competitor profiling, feature parity matrices, win/loss patterns, pricing intelligence, and sales battlecards using proven CI frameworks and market research methodologies.

Design and optimize production-grade NLP pipelines covering text preprocessing, tokenization, NER, sentiment analysis, and transformer-based architectures using spaCy and HuggingFace.

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.

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 building production-grade LLM applications with LangChain. Covers chain composition with LCEL, RAG pipelines, agent systems, LangGraph workflows, prompt engineering, and production deployment with LangSmith observability.

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