Does this skill track token costs and ROI?

Yes, it aggregates token usage across different models and calculates estimated ROI and productivity gains based on time-saved metrics.

Can I generate reports for a specific AI agent?

Absolutely. You can filter analytics by providing a specific Agent ID, allowing for granular performance reporting on individual agents.

What visualization formats does the skill support?

The skill generates structured Markdown reports optimized for the terminal, featuring ASCII bar charts, line charts, and tables for immediate visual clarity.

Do I need the Olakai CLI installed to use this skill?

Yes, this skill relies on the 'olakai' CLI utility to query data and requires a valid authentication state verified via the 'olakai whoami' command.

Can I see blocked or high-risk interactions?

Yes, the Risk Analysis report specifically identifies events with high risk scores, blocked prompts, and sensitive data labels.

Olakai Analytics Reporter

Name: Olakai Analytics Reporter
Author: olakai-ai

byolakai-ai

Analytics & Monitoring

Generates comprehensive analytics reports and KPI insights directly from Olakai data using CLI commands.

About

The Olakai Analytics Reports skill empowers developers and managers to extract deep insights from their AI interactions without leaving the terminal. By leveraging the Olakai CLI, this skill automates the collection and visualization of usage summaries, risk analysis, and ROI metrics. It processes raw JSON data into human-readable Markdown reports featuring ASCII charts, enabling quick assessments of agent performance, token costs, and compliance trends across specific agents or entire organizations.

Key Features

Side-by-side agent performance comparisons with ASCII visualizations
Automated usage summaries and token consumption tracking
Trend analysis for core and custom KPIs with period-over-period comparisons
Detailed risk distribution and sensitivity label reporting
ROI and efficiency metrics including estimated cost savings and time saved
0 GitHub stars

Use Cases

Identifying high-risk AI interactions and compliance violations via the terminal
Auditing AI usage costs and token efficiency across different LLM models
Generating weekly KPI trend reports for stakeholder performance reviews

About

Key Features

Side-by-side agent performance comparisons with ASCII visualizations
Automated usage summaries and token consumption tracking
Trend analysis for core and custom KPIs with period-over-period comparisons
Detailed risk distribution and sensitivity label reporting
ROI and efficiency metrics including estimated cost savings and time saved
0 GitHub stars

Use Cases

Identifying high-risk AI interactions and compliance violations via the terminal
Auditing AI usage costs and token efficiency across different LLM models
Generating weekly KPI trend reports for stakeholder performance reviews