ai-analytics

name	ai-analytics
description	Use when designing AI analytics, dashboards, SaaS AI metrics, NLP analytics, predictive analytics, or executive AI insight workflows. Orchestrates the former granular AI analytics skills as references.
metadata	{"portable":true,"compatible_with":["claude-code","codex"]}

AI Analytics

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, [phone].

Use When

• Design AI analytics products, metric trees, dashboards, and AI insight workflows.
• Plan SaaS AI analytics, NLP analytics, predictive analytics, or reporting surfaces.
• Choose the right analytics reference from consolidated child modules.

Do Not Use When

• The work is not AI-specific or agentic-AI-specific.
• A narrower retained AI parent skill fits the request better.

Required Inputs

• Product, tenant, user, data, risk, and operational context relevant to the AI workflow.
• Target artifact: design, implementation plan, audit, test strategy, UX flow, commercial policy, or runbook.
• Constraints from security, privacy, reliability, billing, support, and compliance stakeholders when relevant.

Workflow

1. Read this SKILL.md first.
2. Load references/routing.md to select the absorbed child reference that matches the task.
3. Load only the selected child reference files needed for the current request.
4. Produce execution-oriented output with assumptions, risks, evidence, and next actions where relevant.

Quality Standards