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.

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.

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.

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.

Maps end-to-end data lineage from source systems through transformations to downstream consumers, enabling impact analysis, debugging data quality issues, and ensuring regulatory compliance.

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.

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.

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

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

Helps users understand the risks of using AI for romantic relationship advice, recognize validation loops and one-sided framing, detect when AI replaces rather than supports human connection, and know when to consult a couples therapist.

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 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.

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.

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 and optimize MongoDB aggregation pipelines for complex data transformations, analytics, and reporting. Covers pipeline stage ordering, index utilization, sharded cluster considerations, and schema design patterns that enable efficient aggregation.

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.

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.