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productivity-analysis

Analyze engineer productivity from GitHub Enterprise, Unblocked, and roster data.

2|Updated Jul 29, 2011
Search Tags:#productivity#GitHub#engineering#calibration#analyze#PRs
One-click install
npx skills add https://github.com/lkosak/dotfiles --skill productivity-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: productivity-analysis
Source: https://github.com/lkosak/dotfiles/tree/main/claude-skills/productivity-analysis
Command: npx skills add https://github.com/lkosak/dotfiles --skill productivity-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams calibrate an engineer's productivity over a defined period by collecting and synthesizing data from GitHub Enterprise, organizational context, and roster information. It enables data-driven performance discussions and objective reviews.

Core Features & Use Cases

  • Cross-source productivity analysis: Combines PR activity, reviews, LOC changes, and project context to generate a holistic view of impact.
  • Period-aware calibration: Focuses on a defined analysis_period (default 2025 calendar year) and uses actual tenure start date for accurate averages.
  • Executive reporting: Produces structured outputs suitable for management discussions and performance calibrations.

Quick Start

Run productivity-analysis by providing engineer_name and optional github_username. The system will fetch data from GitHub Enterprise API, Unblocked context engine, and Plaid roster to generate an assessment for the 2025 calendar year by default.

Frequently Asked Questions about productivity-analysis

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

FAQPage Schema
How do I analyze engineer productivity using GitHub PR data for performance reviews?▼

To calibrate engineer productivity, this Skill aggregates GitHub Enterprise PRs, review activity, and LOC changes over a defined period to generate a structured report for leadership discussions. It leverages the GitHub GraphQL API alongside project context to provide an objective, data-driven view of an engineer's impact.

What data sources are needed to calibrate engineering performance beyond GitHub metrics?▼

Calibrating engineering performance requires GitHub Enterprise PRs alongside Unblocked context and Plaid roster data. Integrating these sources provides a holistic view of an engineer's impact by combining code contributions with organizational tenure and project context.

Can I use GitHub Enterprise data to generate executive reports for year-end engineering reviews?▼

Yes, GitHub Enterprise data can generate executive reports for year-end engineering reviews. The Skill defaults to analyzing the 2025 calendar year and produces structured outputs specifically designed for management discussions and objective performance calibrations.

How does tenure start date affect annual productivity averages calculated from GitHub activity?▼

Tenure start date affects annual productivity averages by ensuring calculations reflect actual employment time rather than the full calendar year. The Skill uses the actual tenure start date from roster data to calculate accurate, period-aware performance metrics for fair calibration.

Do I need to provide a GitHub username to analyze an engineer's pull request activity?▼

You need to provide the engineer_name, while the github_username is optional for analyzing pull request activity. The system uses available roster data and context to map the engineer to their GitHub Enterprise account for fetching PR and review metrics.

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