NexusMCP is a modern Python framework for building MCP-native AI agents, reusable skills, and secure tool-driven workflows with clean orchestration, observability, and extensibility. It is based on the Model Context Protocol, which provides a standardized way for LLM applications to connect to external tools and data sources.
npx skills add aavikshit2007-ops/NexusMCPNexusMCP is a modern Python framework for building MCP-native AI agents, reusable skills, and secure tool-driven workflows with clean orchestration, observability, and extensibility. It is based on the Model Context Protocol, which provides a standardized way for LLM applications to connect to external tools and data sources.
Social media campaign analysis and performance tracking. Calculates engagement rates, ROI, and benchmarks across platforms. Use when analyzing social media performance, calculating engagement rate, measuring campaign ROI, comparing platform metrics, or benchmarking against industry standards. Also use when the user mentions "social media audit," "engagement rate," or "which platform performs best."
Create structured plans for multi-step tasks -- software features, research workflows, events, study plans, or any goal that benefits from breakdown. Also deepens existing plans with interactive sub-agent review. Use when the user says 'plan this', 'create a plan', 'how should we build', 'break this down', or when a brainstorm doc is ready for planning. Use 'deepen the plan' or 'deepening pass' for the deepening flow. For exploratory requests, prefer ce-brainstorm first.
Codex skill for extracting reusable mathematical research skills from Markdown papers.
Interact with NotebookLM for document-based conversations
Design exploration skill for Claude Code. Research-first: scans forums, competitors, and user pain before proposing anything. Three progressive rounds (literal → creative → wild) produce self-executing specs with embedded implementation instructions.
Give AI coding agents (Claude Code, Cursor, Aider, Codex) a structured autonomous loop with guardrails — boundaries, 5 verification gates, 3-layer self-reflection, and autonomous remediation. pip install ouro-loop. Zero dependencies.