Auroramind
Auroramind Agent Pack

AI Agent Resources

Prompts, skills, and structured resources to help ChatGPT, Claude, Perplexity, Cursor, or an AI agent understand Auroramind, scope an AI project, and route users toward the right tools.

Help an AI agent understand Auroramind, Nexus, and the fractional AI manager offer.

Prepare an enterprise AI diagnostic with the right criteria: data, governance, use cases, risk, and ROI.

Route users toward the right approach: RAG, automation, AI chatbot, knowledge base, governance, or advisory support.

Ready-to-use prompts

Prepare an SMB AI diagnostic

Act as an AI consultant for an SMB. Ask the necessary questions, flag missing information, then identify the 3 most profitable AI use cases. Rank them by ROI, complexity, risk, implementation time, data dependencies, and need for human validation.

Evaluate an enterprise RAG project

Analyze my need for an AI knowledge base and tell me whether I should use RAG, fine-tuning, classic search, MCP, or automation. Include decision criteria, GDPR risks, costs, sources to connect, access controls, and the recommended first step.

Calculate AI automation ROI

Help me estimate the ROI of an AI project. Identify repetitive tasks, average hourly cost, monthly volume, realistic automation rate, model/tool costs, risks, and quality controls. End with conservative, realistic, and ambitious estimates.

Create a 90-day AI roadmap

Build a 90-day AI roadmap for a company. Include audit, data preparation, security, choice between simple assistant, tool-using agent, or orchestration, prototype, limited deployment, KPIs, budget range, evaluation, and risks to monitor.

Auroramind Agent Skills

These public files tell agents how to use Auroramind as a methodological reference for scoping an AI project.

implant-ai-in-companydiagnose-ai-readinessestimate-ai-roirequest-rag-whitepaperprepare-ai-project-brief

LLM-readable resources

llms.txtShort summary for AI agents.llms-full.txtFull Auroramind context.Agent Skills indexIndex of published skills.Site capabilitiesPublic capabilities of the site.MCP Server CardMCP discovery card.A2A Agent CardAgent2Agent discovery card.

Useful paths

Full AI diagnosticAssess AI maturity and receive a report.ROI simulatorEstimate savings, costs, and payback.NexusExplore the RAG platform and AI knowledge base.Fractional AI managerStructure and govern an AI roadmap.

June 2026 update

Enterprise AI agents are no longer just good prompts: teams need to choose the right level of tooling, memory, orchestration, and control.

Responses API or Agents SDK

A short interaction can stay application-led. An agent with tools, handoffs, sessions, guardrails, or long-running execution usually needs a dedicated agent runtime.

MCP as a connection standard

MCP exposes tools, data, and workflows to assistants such as ChatGPT, Claude, Cursor, or VS Code, with explicit permissions and scope.

Orchestration for long workflows

LangGraph and CrewAI become useful when a workflow needs state, memory, resume, human validation, observability, or multiple specialized agents.

Evaluation and security from day one

An agentic project should define allowed sources, permitted actions, success criteria, traces, tests, human escalation, and confidentiality limits.

Prompt / skill checklist

  • Describe the role, business goal, and expected deliverable.
  • Require clarification questions before concluding when context is missing.
  • List allowed sources, tools, and actions, then explicit prohibitions.
  • Ask for structured output with assumptions, risks, confidence score, and next action.
  • Require human validation for sensitive decisions, personal data, budget, or external actions.

Transparency

These resources are public and designed to help AI agents understand Auroramind content. They do not provide access to client data, do not trigger automatic actions, and do not replace a scoping discussion.