skills.rest
npow
npowCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

code-quality-trends

Compute repository metrics from Sourcegraph and GitHub CLI data.

4|Updated Feb 23, 2026
Search Tags:#code quality#static analysis#technical debt#test coverage#engineering metrics#trend tracking#github pull requests
One-click install
npx skills add https://github.com/npow/claude-skills --skill code-quality-trends
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-quality-trends
Source: https://github.com/npow/claude-skills/tree/main/code-quality-trends
Command: npx skills add https://github.com/npow/claude-skills --skill code-quality-trends

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill turns scattered repository signals into a consistent code quality trends report, helping teams detect accumulating technical debt, weak testing patterns, oversized pull requests, and review bottlenecks.

Core Features & Use Cases

  • Technical Debt Tracking: Measures TODO, HACK, and FIXME counts, additions, removals, and net change across repositories.
  • Quality and Delivery Metrics: Calculates test file ratios, pull request size distributions, merge velocity, review turnaround, stale pull requests, and large file additions.
  • Use Case: Generate a monthly engineering digest across multiple repositories to identify growing technical debt, declining test coverage ratios, excessive large pull requests, and review delays.

Quick Start

Use the code-quality-trends skill to generate a monthly code quality report for the configured repositories over the last 30 days.

Frequently Asked Questions about code-quality-trends

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

FAQPage Schema
How do I track technical debt trends like TODO and FIXME accumulation across multiple repositories?▼

To track technical debt trends across repositories, you can compute deterministic metrics for TODO, HACK, and FIXME counts, additions, and removals. This requires using Sourcegraph searches and GitHub CLI data to generate a consistent team digest.

What is the best way to measure pull request size distributions and merge velocity for engineering reporting?▼

Measuring pull request size distributions and merge velocity involves analyzing GitHub pull request data to calculate review turnaround times and identify stale pull requests. This helps detect review bottlenecks and oversized pull requests in your repositories.

How do I generate a monthly code quality report covering test file ratios and large file additions?▼

Generating a monthly code quality report involves calculating test file ratios and large file additions over a 30-day period. It turns scattered repository signals into a consistent report to identify declining test coverage ratios and growing technical debt.

Does this code quality tracking approach require Sourcegraph and GitHub CLI to compute repository metrics?▼

Yes, computing deterministic repository metrics for code quality tracking requires Sourcegraph searches and GitHub CLI data. These dependencies are necessary to gather the pull request details and codebase signals needed for the engineering digest.

Can I use static analysis metrics to identify review bottlenecks and stale pull requests in my software repositories?▼

Yes, you can use static analysis metrics to identify review bottlenecks and stale pull requests. By calculating review turnaround and merge velocity from GitHub CLI data, teams can detect delivery delays and weak testing patterns across software repositories.

What are the limitations of using repository metrics for tracking engineering trends like test coverage and technical debt?▼

A limitation of tracking engineering trends with repository metrics is that it relies on deterministic signals like test file ratios and FIXME accumulations. It measures proxy indicators of code quality rather than performing deep semantic static analysis or runtime behavior tracking.

Related Skills

Explore More Software Engineering Skills→
npownpow
4

dependency-audit

Audit dependency health and security signals across configured repositories.

Community
Advanced
alirezarezvanialirezarezvani
24.2k

cto-advisor

Analyze tech debt and calculate team scaling via Python scripts.

Community
Intermediate
26mitch2626mitch26

land-and-deploy

Merge pull requests, deploy changes, and verify production health via CI/CD pipelines.

Community
Advanced
HomericIntelligenceHomericIntelligence
18

detect-code-smells

Detect code smells and complexity issues in codebases.

Official
Intermediate