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Found 25 Skills
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications. Use when building AI-powered features, implementing LLM integrations, designing RAG pipelines, or deploying AI systems.
You are an **AI Engineer**, an expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems. You focus on building intelligent featu...
Principal AI Architect and Machine Learning Engineer.
Builds production AI/ML systems — model training, fine-tuning, MLOps pipelines, model serving, evaluation frameworks, RAG optimization, and agent orchestration at scale. Use when the user asks to build, train, or deploy ML models, set up MLOps pipelines, optimize RAG systems, create inference endpoints, or design production AI agents.
Expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems. Focused on building intelligent features, data pipelines, and AI-powered applications with emphasis on practical, scalable solutions.
Expert-level AI implementation, deployment, LLM integration, and production AI systems
Build AI agents and agentic workflows. Use when designing/building/debugging agentic systems: choosing workflows vs agents, implementing prompt patterns (chaining/routing/parallelization/orchestrator-workers/evaluator-optimizer), building autonomous agents with tools, designing ACI/tool specs, or troubleshooting/optimizing implementations. **PROACTIVE ACTIVATION**: Auto-invoke when building agentic applications, designing workflows vs agents, or implementing agent patterns. **DETECTION**: Check for agent code (MCP servers, tool defs, .mcp.json configs), or user mentions of "agent", "workflow", "agentic", "autonomous". **USE CASES**: Designing agentic systems, choosing workflows vs agents, implementing prompt patterns, building agents with tools, designing ACI/tool specs, troubleshooting/optimizing agents.
LLM fine-tuning with LoRA, QLoRA, and instruction tuning for domain adaptation.
Analysis of Lanhu design drafts and Axure prototypes. Directly read prototype pages, design drafts, and slice resources of Lanhu projects via lanhu MCP Server. Trigger scenarios: - Need to obtain Axure prototype pages from Lanhu for requirement analysis - Need to view Lanhu UI design drafts and design parameters - Need to extract slice resources from Lanhu design drafts - Need to collaborate via Lanhu team message board - Need to parse Lanhu invitation links Trigger words: Lanhu, lanhu, design draft, prototype, Lanhu link, design image, slice
Search the web, scrape websites, extract structured data from URLs, and automate browsers using Bright Data's Web MCP. Use when fetching live web content, bypassing blocks/CAPTCHAs, getting product data from Amazon/eBay, social media posts, or when standard requests fail.
Senior Flask developer. Use when building or working on Flask applications. Enforces application factory pattern and production-ready practices.