Optimize prompts for Claude's latest models (Claude 5 family — Opus 5, Sonnet 5, Fable 5 — plus the still-active Opus 4.8, Sonnet 4.6, and Haiku 4.5) using Anthropic's official prompt-engineering guidance. Use when users want to improve, refine, debug, or create prompts or system prompts for Claude. Triggers include requests to optimize/fix/refine a prompt, make a prompt more effective, write a system prompt, improve instruction following, control verbosity or output formatting, tune effort or thinking depth, fix tool-use/agentic/subagent behavior, migrate prompts to a newer Claude model, or migrate away from prefilled responses. Also use when users report Claude being too verbose, ignoring or partially applying instructions, over- or under-using tools, overthinking, overengineering, spawning too many subagents, hallucinating about code, or producing generic "AI slop" output.
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
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)
Core consulting thinking frameworks and methodologies for structuring business problems, communicating findings, analyzing strategy, building financial models, and designing operations. Use when any agent or command needs to apply MECE decomposition, pyramid principle, hypothesis-driven analysis, issue trees, SCR communication, Porter's Five Forces, TAM/SAM/SOM market sizing, value chain analysis, NPV/IRR decision criteria, build/buy/partner evaluation, RACI matrices, synergy sizing, valuation triangulation, price-volume-mix decomposition, discount waterfalls, or any standard consulting framework. This skill provides procedural guidance — not just framework names, but how to apply them correctly.
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, sensitivity analysis, 1031 exchange planning, cost segregation, depreciation, real estate tax strategy, market comparison and scoring, rental property screening, deal screening, investor report generation, real estate financial modeling, rent-to-price analysis, development feasibility, value-add underwriting, API integration for real estate data (Census, Redfin, Rentcast, Mashvisor, AirDNA, ATTOM), or any real estate investment financial analysis task.
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.