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.

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

A generalist skill that helps users interact with AI safely across all sensitive domains. Covers health, finance, legal, mental health, nutrition, parenting, relationships, and general AI literacy. Teaches critical evaluation of AI outputs, recognizes when professional help is needed, and prevents over-reliance on AI for consequential decisions.

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.

Helps parents, educators, and teens themselves understand the specific risks of AI chatbot companions for adolescents, recognize warning signs of problematic AI relationships, and implement age-appropriate safety measures.

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.

Helps users understand the narrow safe zone for AI in mental health contexts, recognize dangerous interaction patterns, identify parasocial relationship risks, and know when AI support crosses into territory that requires a human professional.

Designs production-grade Retrieval-Augmented Generation pipelines including chunking strategies, embedding model selection, vector database architecture, hybrid search, reranking, and hallucination reduction techniques.

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

Systematic optimization of machine learning model hyperparameters using Bayesian optimization, multi-fidelity methods, and distributed search strategies to find optimal configurations efficiently.

Design intelligent model routing systems that optimize cost, latency, and quality across multiple LLM providers. Covers cascading strategies, confidence-based escalation, prompt caching economics, and fallback architectures.

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.

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.

Ensure offline/online feature consistency in ML systems by designing validation pipelines, detecting training-serving skew, and implementing point-in-time correctness guarantees across feature store architectures.

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

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.

Master SQL window functions for advanced analytical queries including ranking, running aggregates, time-series analysis, and cohort calculations across PostgreSQL, Snowflake, and BigQuery.

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

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

Design data activation pipelines that sync analytical data from cloud data warehouses back into operational SaaS tools like CRMs, marketing platforms, and support systems.

Design and implement structured deep learning training workflows using PyTorch Lightning, covering LightningModule architecture, distributed training strategies, experiment tracking, checkpoint management, and production model deployment.

Design and optimize graph database schemas, Cypher queries, and production deployments for knowledge graphs, recommendation engines, fraud detection, and network analysis applications.

Designs robust data pipelines covering ETL/ELT patterns, orchestration (Airflow/Dagster/Prefect), data quality validation, lineage tracking, idempotency, streaming vs batch architecture, and data lakehouse design for production workloads.