Metrics Review

Name: Metrics Review
Availability: InStock
Author: mberto10

Analyze tracked behaviors and outcomes with SQL queries for trend comparison.

Updated Aug 23, 2026

Search Tags:#metrics #compliance #data analysis #feedback loop #review #progress

Ready to run

npx skills add https://github.com/mberto10/mberto-compound --skill metrics-review

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Metrics Review
Source: https://github.com/mberto10/mberto-compound/tree/main/plugins/daily-metrics/skills/metrics-review
Command: npx skills add https://github.com/mberto10/mberto-compound --skill metrics-review

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the friction of tracking behaviors and outcomes without a structured review process, ensuring that data collection leads to actionable insights and continuous improvement.

Core Features & Use Cases

Automated Compliance Calculation: Calculates adherence to tracked behaviors.
Trend Analysis: Compares current performance against historical data (weekly, 4-week average).
Insight Generation: Helps identify what's working, what's not, and why, based on data.
Structured Review Cadence: Supports daily, weekly, and monthly review processes.
Use Case: A user wants to understand their progress on a new habit, like daily exercise, and see how it correlates with their energy levels throughout the week. This skill can calculate their exercise compliance, show trends in energy levels, and help them determine if the habit is having the desired effect.

Quick Start

Use the metrics review skill to analyze my weekly progress on my key behaviors and outcomes.

Frequently Asked Questions about Metrics Review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze tracked behaviors and outcomes to close the feedback loop?▼

Analyzing tracked behaviors and outcomes to close the feedback loop requires calculating behavior compliance and identifying outcome trends using SQL queries for data aggregation. This process extracts signal from noise to facilitate data-driven iteration and actionable insights.

What is the best way to calculate behavior compliance for daily and weekly reviews?▼

The best way to calculate behavior compliance for daily and weekly reviews is using automated compliance calculation against historical data. Comparing current performance against a 4-week average identifies outcome trends and helps determine what actions are working.

How do I compare current performance against historical data using SQL?▼

Comparing current performance against historical data using SQL involves executing queries for data aggregation and trend comparison. This structured review cadence calculates adherence to tracked behaviors and identifies outcome trends for continuous improvement.

Can I use data aggregation to identify trends in my habit tracking progress?▼

Yes, you can use data aggregation to identify trends in your habit tracking progress. By comparing current performance against a 4-week average, the review process calculates exercise compliance, shows energy level trends, and helps determine if the habit is having the desired effect.

When do I need a structured review cadence for data-driven iteration?▼

You need a structured review cadence for data-driven iteration when friction from tracking behaviors and outcomes prevents actionable insights. Supporting daily, weekly, and monthly review processes ensures data collection leads to continuous improvement and identifies what is not working.

Does this metrics review process require SQL queries to extract signal from noise?▼

Yes, this metrics review process requires SQL queries to extract signal from noise. Utilizing SQL queries for data aggregation and trend comparison calculates behavior compliance and identifies outcome trends, ensuring data collection translates into actionable insights.