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Found 2,194 Skills
Design or audit AI-first help centers/knowledge bases/FAQs, including taxonomy, article templates, analytics, and AI support (RAG, chatbot, escalation), using 2025-2026 best practices
Query decomposition for multi-concept retrieval. Use when handling complex queries spanning multiple topics, implementing multi-hop retrieval, or improving coverage for compound questions.
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
Comprehensive testing specialization covering test strategy, automation, TDD methodology, test writing, and web app testing. Use when setting up test infrastructure, writing tests, implementing TDD workflows, analyzing coverage, integrating tests into CI/CD, or testing web applications with Playwright. Framework-agnostic approach with framework-specific guidance via reference files.
Validate code quality, test coverage, performance, and security. Use when verifying implemented features meet all standards and requirements before marking complete.
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.
Prepares and audits high-quality datasets for AI/RAG applications. Cleans noise, structure data, and ensures privacy compliance in knowledge bases.
Perform code reviews following best practices from Code Smells and The Pragmatic Programmer. Use when asked to "review this code", "check for code smells", "review my PR", "audit the codebase", or need quality feedback on code changes. Supports both full codebase audits and focused PR/diff reviews. Outputs structured markdown reports grouped by severity.
Test coverage, code quality, defect metrics, and QA KPIs
Choose and combine Eve storage primitives to give agents persistent memory — short-term workspace, medium-term attachments and threads, long-term org docs and filesystem. Use when designing how agents remember, retrieve, and share knowledge.