Master the AI Engineer Stack: A professional-grade roadmap for Agentic Generative AI. Features Python, LangGraph, Multi-Agent workflows, Scalable RAG, Semantic Memory, and MCP integrations using LLMs from Hugging Face and OpenAI.
ai-engineer-stack is a Claude Code agent skill that master the AI Engineer Stack: A professional-grade roadmap for Agentic Generative AI. Features Python, LangGraph, Multi-Agent workflows, Scalable RAG, Semantic Memory, and MCP integrations using LLMs from Hugging Face and OpenAI.
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Master the AI Engineer Stack: A professional-grade roadmap for Agentic Generative AI. Features Python, LangGraph, Multi-Agent workflows, Scalable RAG, Semantic Memory, and MCP integrations using LLMs from Hugging Face and OpenAI.
Source: https://github.com/findashu/ai-engineer-stack
Discovered from GitHub repositories pushed in the last 24 hours for agent skills, Claude/Codex/Gemini workflows, MCP tooling, and adjacent AI-agent automation.
Agent skill repository discovered by 10x-chat research.
Verification loop for Quarkus projects: build, static analysis, tests with coverage, security scans, native compilation, and diff review before release or PR.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.