expert-ai-skills

Route prediction and forecasting problems to the right method. Covers 7 families: Monte Carlo simulation, statistical forecasting (ARIMA/exponential smoothing), machine learning, Bayesian inference, crowd aggregation, causal inference, and first-principles modeling. Use when you need to predict a future outcome, quantify uncertainty, forecast time-series, update a belief with evidence, infer a cause, or synthesize expert opinions. Combines Monte Carlo Predictor, bootstrap, Bayesian update, and exponential smoothing as callable tools; routes to external methods (ML, markets, causal, physics) when those are the right fit.

Monte Carlo prediction framework for evaluating any project, with a real simulation engine. Use when the user wants to validate decisions, predict outcomes, find optimal paths, detect design divergences, or stress-test a project's direction. Activates on: 'predict', 'Monte Carlo', 'scenario analysis', 'what could go wrong', 'best path', 'validate direction', 'risk analysis', 'forecast', 'project trajectory', 'stress test', 'decision matrix', 'should I migrate', 'compare options'.

Your personal AI operating system — a digital twin that advocates for your interests 24/7. Orchestrates sub-agents, maintains persistent memory, forecasts opportunities, guards against threats, and never gives up on finding answers. Built on OpenClaw. Activates on: 'orchestrator', 'my AI', 'digital twin', 'second brain', 'spin up agent', 'find me', 'watch for', 'optimize my', 'what should I do'.

Universal AI Harness — a meta-framework that wraps any AI model to reduce token waste, ensure spec-driven thinking, maintain persistent memory, and produce calibrated, high-accuracy outputs. Combines BMAD spec-driven methodology, Deep Confidence reasoning, Monte Carlo validation, ReAct execution, and continuous learning. Use for any complex task, decision, or build. Activates on: 'think first', 'harness mode', 'spec-driven', 'BMAD', 'deep reasoning', 'plan before acting', 'structured thinking', 'truth-seeking'.

Deep Confidence Harness — a thinking, planning, and execution framework that forces structured reasoning before acting. Combines Monte Carlo scenario analysis, calibrated confidence, multi-perspective debate, and optimal path planning. Use before any complex decision, build, or task. Activates on: 'deep confidence', 'think before you act', 'plan first', 'Atlas mode', 'reason through this', 'what should I do', 'think this through', 'best approach', 'reason carefully', 'plan and execute'.

OpenClaw personal AI assistant configuration for life organization and income generation. Use when setting up, configuring, or instructing an OpenClaw agent named Henry to manage daily life, finances, tasks, calendar, and money-making activities. Activates on: 'Henry', 'OpenClaw Henry', 'my AI assistant', 'organize my life', 'make money with AI', 'set up Henry', 'Henry config'.

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.

API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.

Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

Code review guidelines covering code quality, security, and best practices.

When the user wants to write, rewrite, or improve marketing copy for any page — including homepage, landing pages, pricing pages, feature pages, about pages, or product pages. Also use when the user says "write copy for," "improve this copy," "rewrite this page," "marketing copy," "headline help," or "CTA copy." For email copy, see email-sequence. For popup copy, see popup-cro.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.

Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.

Docker containerization expert with deep knowledge of multi-stage builds, image optimization, container security, Docker Compose orchestration, and production deployment patterns. Use PROACTIVELY for Dockerfile optimization, container issues, image size problems, security hardening, networking, and orchestration challenges.

This skill should be used when the user asks to "learn ethical hacking", "understand penetration testing lifecycle", "perform reconnaissance", "conduct security scanning", "exploit vulnerabilities", or "write penetration test reports". It provides comprehensive ethical hacking methodology and techniques.

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.

Next.js App Router principles. Server Components, data fetching, routing patterns.

This skill should be used when the user asks to "plan a penetration test", "create a security assessment checklist", "prepare for penetration testing", "define pentest scope", "follow security testing best practices", or needs a structured methodology for penetration testing engagements.

Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.

Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," or "SEO health check." For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Use when implementing any feature or bugfix, before writing implementation code