Conversational skill that interviews the user to capture professional experience and build/update their career-profile.json. Use when the user says "update my resume", "add my recent experience", "refresh my profile", "capture my work history", "build my resume", "add a new job to my resume", or wants to create or modify their professional profile data. When to Use: - User wants to add new experience, skills, projects, or achievements - User wants to create their career profile from scratch - User wants to update existing profile entries - User mentions they changed jobs, got promoted, or completed a project When NOT to Use: - User wants to generate/format a resume (use /resume-generator command) - User wants career advice (use career-director agent) - User wants to search for jobs (use job-search agent)
End-to-end real estate investment analysis skill. Use when users ask to analyze a property deal, run the numbers on a rental, evaluate real estate investments, build a pro forma, compare markets, calculate cap rate, cash-on-cash return, IRR, NOI, DSCR, equity multiple, or GRM. Also triggers on: BRRRR analysis, house hack evaluation, short-term rental (STR/Airbnb) analysis, commercial underwriting, multifamily deal analysis, syndication waterfall modeling, Monte Carlo simulation for real estate, sensitivity analysis on a deal, 1031 exchange planning, cost segregation analysis, depreciation calculations, real estate tax strategy, market comparison and scoring, rental property screening, deal screening, investor report generation, real estate financial modeling, property type comparison, rent-to-price analysis, development feasibility, land analysis, value-add underwriting, API integration for real estate data (Zillow, Redfin, AirDNA, Mashvisor, ATTOM, Rentcast, Census), or any real estate investment financial a
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime
Comprehensive guide for Deep Learning with Keras 3 (Multi-Backend: JAX, TensorFlow, PyTorch). Use when building neural networks, CNNs for computer vision, RNNs/Transformers for NLP, time series forecasting, or generative models (VAEs, GANs). Covers model building (Sequential/Functional/Subclassing APIs), custom training loops, data augmentation, transfer learning, and production best practices.
Set up, install, configure, and deploy OpenClaw (formerly ClawdBot/MoltBot) — a personal AI assistant that runs on your own devices and connects to messaging channels. Use when users ask to "set up OpenClaw," "install ClawdBot," "install MoltBot," "deploy a personal AI assistant," "configure OpenClaw on Mac," "deploy OpenClaw to VPS," "set up OpenClaw on Hostinger," "connect OpenClaw to Telegram," "configure iMessage with OpenClaw," or any variation involving OpenClaw installation, gateway configuration, channel setup, Anthropic auth, or security hardening. Also triggers on "openclaw onboard," "openclaw doctor," "openclaw security audit," troubleshooting OpenClaw deployments, OpenClaw security, OpenClaw cost control, or ClawHub skills safety.
Optimize prompts for Claude 4.x models using Anthropic's official best practices. Use when users want to improve, refine, or create effective prompts for Claude. Triggers include requests to optimize prompts, make prompts more effective, fix underperforming prompts, create system prompts, improve instruction following, reduce verbosity, control output formatting, or enhance agentic/tool-use behaviors. Also use when users report Claude is being too verbose, not following instructions, not using tools properly, or producing generic outputs.
Reinforcement Learning best practices for Python using modern libraries (Stable-Baselines3, RLlib, Gymnasium). Use when: - Implementing RL algorithms (PPO, SAC, DQN, TD3, A2C) - Creating custom Gymnasium environments - Training, debugging, or evaluating RL agents - Setting up hyperparameter tuning for RL - Deploying RL models to production
Build applications using the x402 protocol — Coinbase's open standard for HTTP-native stablecoin payments using the HTTP 402 status code. Use this skill when: - Creating APIs that require USDC payments per request (seller/server side) - Building clients or AI agents that pay for x402-protected resources (buyer/client side) - Implementing MCP servers with paid tools for Claude Desktop - Adding payment middleware to Express, Hono, or Next.js applications - Working with Base (EVM) or Solana (SVM) payment flows - Building machine-to-machine or agent-to-agent payment systems - Integrating micropayments, pay-per-use billing, or paid API access Triggers: x402, HTTP 402, payment required, USDC payments, micropayments, pay-per-use API, agentic payments, stablecoin payments, paid API endpoint, paywall middleware