skills.rest
lucasxf
lucasxfCOMMUNITY
Explore all skills by this author →
GitHub

productivity-metrics

Interpret productivity data and compare pre-AI baselines against DORA metrics.

3|Updated Jan 14, 2026
Search Tags:#DORA metrics#productivity analysis#delivery velocity#AI impact#productivity trends
One-click install
npx skills add https://github.com/lucasxf/engineering-daybook --skill productivity-metrics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: productivity-metrics
Source: https://github.com/lucasxf/engineering-daybook/tree/main/.claude/skills/productivity-metrics
Command: npx skills add https://github.com/lucasxf/engineering-daybook --skill productivity-metrics

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill interprets productivity data, analyzes AI impact on delivery velocity, compares periods against baselines, and answers questions about productivity trends, AI impact, and DORA metrics.

Core Features & Use Cases

  • Data Interpretation: Analyze productivity data for insights and decision support.
  • AI Impact Analysis: Compare pre-AI and post-AI periods to understand the effect of AI on productivity.
  • Baseline Reconstruction: Guide users in setting up and interpreting pre-AI baselines.
  • DORA Metrics Interpretation: Explain and provide context around DORA metrics for better decision-making.

Quick Start

Ask the productivity-metrics skill to compare productivity trends before and after the adoption of AI tools.

Frequently Asked Questions about productivity-metrics

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

FAQPage Schema
How do I measure AI impact on engineering delivery velocity?▼

To measure AI impact on delivery velocity, you need to compare your post-AI adoption productivity data against a reconstructed pre-AI baseline to identify meaningful trends and shifts in output.

How do I set up a pre-AI baseline for productivity analysis?▼

Setting up a pre-AI baseline for productivity analysis involves gathering historical delivery data prior to AI tool adoption, which establishes a reference point to compare against current periods.

What are DORA metrics and how do I interpret them for my team?▼

DORA metrics are standard delivery performance indicators, and interpreting them involves analyzing deployment frequency, lead time, change failure rate, and recovery time to guide engineering decisions.

Can I compare productivity trends across different time periods?▼

Yes, you can compare productivity trends across different time periods by evaluating current data against established baselines to understand how changes like AI adoption affect output.

What is the best way to analyze software engineering productivity trends?▼

The best way to analyze software engineering productivity trends is by combining DORA metrics interpretation with historical baseline comparisons to provide context for delivery velocity changes.

Related Skills

Explore More Software Engineering Skills→
bgschillerbgschiller

check-pipeline

Diagnose GitLab CI pipeline failures by analyzing statuses and job logs.

Community
Intermediate
aget-frameworkaget-framework
11

aget-generate-report

Generate structured reports with metrics, findings, and visualizations for specific audiences.

Official
Intermediate
npownpow
4

dora-lite-report

Generate weekly DORA metrics reports from API and Spinnaker data.

Community
Advanced
devmuslimdevmuslim

delivery-kpis

Define and track DORA and flow metrics for product delivery teams.

Community
Intermediate