Integrate LLMs, orchestrate agent workflows, and deploy MCP servers.
npx skills add https://github.com/gajakannan/nebula-crm --skill ai-engineer-gajakannan
Please help me install this Agent Skill. Skill: ai-engineer Source: https://github.com/gajakannan/nebula-crm/tree/main/agents/ai-engineer Command: npx skills add https://github.com/gajakannan/nebula-crm --skill ai-engineer-gajakannan
This skill helps teams build the AI intelligence layer by connecting LLMs, orchestrating agent workflows, and exposing MCP servers to power intelligent automation.
Install Python 3.11+, review the repository structure (neuron/, planning-mds/, agents/ai-engineer/), and begin by inspecting SKILL.md for guidelines. Then run unit tests with pytest and start local MCP servers to validate integrations.
High-intent search queries and answers about installing and using this skill.
Agent workflow orchestration involves designing and managing multi-step processes that connect LLMs to backend interactions. You orchestrate these workflows within neuron components to route models and execute tooling across domains.
An MCP server implementation provides endpoints to expose CRM data and tooling to AI agents. It acts as the bridge connecting backend systems to the agent workflows within the AI intelligence layer.
LLM integration connects cloud and self-hosted models to the neuron component to enable scalable AI capabilities. Model routing requirements are satisfied by configuring the intelligence layer to direct requests appropriately.
Prompt engineering and testing allows you to create, test, and version prompts using robust tooling and mocks. This ensures reliable LLM outputs before deploying agent workflows to production environments.
Python 3.11+ is required to build the AI intelligence layer with LLMs. You must also review the repository structure and run unit tests with pytest to validate integrations before starting local MCP servers.
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