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Found 1,020 Skills
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
Create, optimize, update, and validate AGENTS.md files with maximum token efficiency. Use when the user asks to (1) create new AGENTS.md files for any repository, (2) optimize/condense existing AGENTS.md to reduce token count, (3) update/refresh AGENTS.md to sync with codebase changes, (4) validate AGENTS.md quality and completeness, or (5) improve AGENTS.md files to be more effective for AI agents. Always generates token-efficient, condensed output focused on actionable commands and patterns while maintaining model-agnostic language.
Analyze 8-K filings to extract material events and corporate changes using Octagon MCP. Use when tracking real-time corporate disclosures, M&A announcements, leadership changes, earnings releases, and other material events requiring immediate investor attention.
Node.js backend patterns: framework selection, layered architecture, TypeScript, validation, error handling, security, production deployment. Use when building REST APIs, Express/Fastify servers, microservices, or server-side TypeScript.
Transforms vague or rough prompts into precise, structured AI instructions. Use when asked to "refine prompt", "improve prompt", "make this prompt better", "promptify", "optimize prompt", "rewrite prompt", "enhance prompt", or "sharpen instructions".
Model software around the business domain using bounded contexts, aggregates, and ubiquitous language. Use when the user mentions "domain modeling", "bounded context", "aggregate root", "ubiquitous language", or "anti-corruption layer". Covers entities vs value objects, domain events, and context mapping strategies. For architecture layers, see clean-architecture. For complexity, see software-design-philosophy.
Query Developer Experience (DX) data via the DX Data MCP server PostgreSQL database. Use this skill when analyzing developer productivity metrics, team performance, PR/code review metrics, deployment frequency, incident data, AI tool adoption, survey responses, DORA metrics, or any engineering analytics. Triggers on questions about DX scores, team comparisons, cycle times, code quality, developer sentiment, AI coding assistant adoption, sprint velocity, or engineering KPIs.
Use when adding LangChain-based LLM routes or services in Python or Next.js stacks; pair with architect-stack-selector.
Deep repository analysis skill for Z.AI Zread MCP.
Apply when implementing caching logic, CDN configuration, or performance optimization for a headless VTEX storefront. Covers which VTEX APIs can be cached (Intelligent Search, Catalog) versus which must never be cached (Checkout, Profile, OMS), stale-while-revalidate patterns, cache invalidation, and BFF-level caching. Use for any headless project that needs TTL rules and caching strategy guidance.
Persistent browser interaction through a normal Node.js Playwright script for fast iterative web UI debugging.
Manage server resources via BT Panel API. Supports website management, Docker orchestration, database operation and maintenance, and file management. Features automatic firewall bypass and session sniffing capabilities.